Dynamic model selection in speech-to-text processing

ABSTRACT

An apparatus includes processor(s) to: use an acoustic model to generate a first set of probabilities of speech sounds uttered within speech audio; derive at least a first candidate word most likely spoken in the speech audio using the first set; analyze the first set to derive a degree of uncertainty therefor; compare the degree of uncertainty to a threshold; in response to at least the degree of uncertainty being less than the threshold, select the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold, select, as the next word most likely spoken in the speech audio, a second candidate word indicated as being most likely spoken based on a second set of probabilities generated by a language model; and add the next word most likely spoken to a transcript.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of, and claims the benefit of priority under 35 U.S.C. § 120 to, U.S. patent application Ser. No. 17/138,521 filed Dec. 30, 2020, and entitled “Speech Audio Pre-Processing Segmentation”; which is a continuation of, and claims the benefit of priority under 35 U.S.C. § 120 to, U.S. patent application Ser. No. 17/138,445 filed Dec. 30, 2020, and entitled “Speech Audio Pre-Processing Segmentation”; which claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Application Ser. No. 62/991,275 filed Mar. 18, 2020, and entitled “A Pipeline for Information Extraction from Audio Files”; each of which is incorporated herein by reference in its entirety for all purposes.

BACKGROUND

It has become commonplace to perform automated speech-to-text conversion of captured speech audio. Such a conversion to text may be performed as part of receiving verbal commands used as input for the provision of various voice-controlled online services. Such a conversion to text may be performed as part of indexing and/or memorializing the contents of recorded voice messages or of phone conversations. Or, such a conversion to text may be used as part of various automated analyses of the contents of conversations or verbal presentations, such as an evaluation of the quality of service provided in telephone service calls, of the efficiency or effectiveness of communication in emergency services calls, or of the audience participation and/or reaction to a verbal presentation.

Regardless of the purpose for performing automated speech-to-text conversion, a longstanding challenge has been improving its accuracy. As will be familiar to those skilled in the art, there are numerous challenges, including and not limited to, quality issues with the devices used to capture speech audio, high environmental noise levels, languages having multiple dialects, differences in regional accents, differences in idiomatic expressions, and/or per-person differences in pronunciation, speed of speaking, speaking volume, speech impediments, etc. Over time, various significant improvements have been made to acoustic models and language models that are used. However, there remains challenges in this technical field and the preprocessing used to divide streamed speech audio and/or lengthy recorded speech audio into segments has seen comparatively little improvement.

SUMMARY

This summary is not intended to identify only key or essential features of the described subject matter, nor is it intended to be used in isolation to determine the scope of the described subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

An apparatus includes at least one processor and a storage to store instructions that, when executed by the at least one processor, cause the at least one processor to perform operations including receive, from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio. In response to the request, the at least one processor is also caused to perform speech-to-text processing operations including: use an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; derive at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyze the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; compare the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, the at least one processor is caused to select the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, the at least one processor is caused to select, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and add the next word most likely spoken to a transcript of the speech audio.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium includes instructions operable to cause at least one processor to perform operations including receive, from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio. In response to the request, the at least one processor is also caused to perform speech-to-text processing operations including: use an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; derive at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyze the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; compare the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, the at least one processor is caused to select the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, the at least one processor is caused to select, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and add the next word most likely spoken to a transcript of the speech audio.

Derivation of at least the first candidate word may include deriving a set of candidate words most likely spoken in the speech audio from the first set of probabilities; the set of candidate words may include the first candidate word and the second candidate word; and the second candidate word may be indicated as being most likely spoken among the set of candidate words based on the second set of probabilities generated by the language model to correspond to the set of candidate words.

The at least one processor may be caused, in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, to refrain from expending processing resources to use the language model to generate the second set of probabilities.

The at least one processor may be caused to select the first candidate word or the second candidate word based on a combination of the degree of uncertainty of the first set of probabilities and at least a subset of the probabilities within the second set of probabilities.

The language model may be based on an n-gram corpus comprising n-grams correlated to corresponding probabilities of use in a pre-selected language. The use of the language model to generate the second set of probabilities may include the at least one processor performing operations including: for each candidate word of the set of candidate words, generate a corresponding candidate n-gram of a set of candidate n-grams that comprises a combination of the candidate word and at least one preceding word spoken in the speech audio; for each candidate n-gram, search the n-gram corpus for the candidate n-gram to retrieve the corresponding probability for inclusion in the second set of probabilities; determine which candidate n-gram of the set of candidate n-grams corresponds to the highest probability among the second set of probabilities; and indicate the candidate word that corresponds to the candidate n-gram that corresponds to the highest probability of the second set of probabilities as being the second candidate word.

The acoustic model may accept indications of instances of acoustic features of a pre-selected set of acoustic features occurring within the portion of speech audio. Use of the acoustic model to generate the first set of probabilities may include the at least one processor performing operations including: use a feature detector to detect instances of occurrence of the pre-selected set of acoustic features to generate at least one feature vector that provides the indications of acoustic features occurring within the portion of speech audio; and provide the at least one feature vector to the acoustic model as input to generate the first set of probabilities.

The acoustic model may be implemented with a neural network; and the neural network may include a connectionist temporal classification (CTC) output to provide an indication of a pause between speech sounds of the next word for inclusion in the at least one feature vector.

The first set of probabilities may include at least one probability distribution indicative of relative probabilities of utterance of each grapheme of a pre-selected set of graphemes at a time during pronunciation of speech sounds of the next word; and analysis of the first set of probabilities to derive the degree of uncertainty may include deriving a degree of entropy or perplexity for each probability distribution of the at least one probability distribution.

Each grapheme of the pre-selected set of graphemes may correspond to a portion of a phoneme, a single phoneme or a combination of phonemes.

Each grapheme of the pre-selected set of graphemes may correspond to a single text character of a pre-selected set of text characters or a combination of text characters of the pre-selected set of text characters; and the pre-selected set of text characters may include a blank symbol indicative of a pause between speech sounds.

A computer-implemented method includes receiving, by at least one processor, and from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio. The method also includes, in response to the request, performing speech-to-text processing operations including: using, by the at least one processor, an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; deriving, by the at least one processor, at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyzing, by the at least one processor, the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; comparing, by the at least one processor, the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, selecting, by the at least one processor, the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, selecting, by the at least one processor, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and adding the next word most likely spoken to a transcript of the speech audio.

Deriving at least the first candidate word may include deriving a set of candidate words most likely spoken in the speech audio from the first set of probabilities; the set of candidate words may include the first candidate word and the second candidate word; and the second candidate word may be indicated as being most likely spoken among the set of candidate words based on the second set of probabilities generated by the language model to correspond to the set of candidate words.

The method may include, in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, refraining from expending processing resources to use the language model to generate the second set of probabilities.

The method may include selecting the first candidate word or the second candidate word based on a combination of the degree of uncertainty of the first set of probabilities and at least a subset of the probabilities within the second set of probabilities.

The language model may be based on an n-gram corpus comprising n-grams correlated to corresponding probabilities of use in a pre-selected language. Using the language model to generate the second set of probabilities may include performing operations including: for each candidate word of the set of candidate words, generating a corresponding candidate n-gram of a set of candidate n-grams that comprises a combination of the candidate word and at least one preceding word spoken in the speech audio; for each candidate n-gram, searching the n-gram corpus for the candidate n-gram to retrieve the corresponding probability for inclusion in the second set of probabilities; determining which candidate n-gram of the set of candidate n-grams corresponds to the highest probability among the second set of probabilities; and indicating the candidate word that corresponds to the candidate n-gram that corresponds to the highest probability of the second set of probabilities as being the second candidate word.

The acoustic model may accept indications of instances of acoustic features of a pre-selected set of acoustic features occurring within the portion of speech audio. Using the acoustic model to generate the first set of probabilities may include performing operations including: using a feature detector to detect instances of occurrence of the pre-selected set of acoustic features to generate at least one feature vector that provides the indications of acoustic features occurring within the portion of speech audio; and providing the at least one feature vector to the acoustic model as input to generate the first set of probabilities.

The acoustic model may be implemented with a neural network; and the neural network may include a connectionist temporal classification (CTC) output to provide an indication of a pause between speech sounds of the next word for inclusion in the at least one feature vector.

The first set of probabilities may include at least one probability distribution indicative of relative probabilities of utterance of each grapheme of a pre-selected set of graphemes at a time during pronunciation of speech sounds of the next word; and analyzing the first set of probabilities to derive the degree of uncertainty may include deriving a degree of entropy or perplexity for each probability distribution of the at least one probability distribution.

Each grapheme of the pre-selected set of graphemes may correspond to a portion of a phoneme, a single phoneme or a combination of phonemes.

Each grapheme of the pre-selected set of graphemes may correspond to a single text character of a pre-selected set of text characters or a combination of text characters of the pre-selected set of text characters; and the pre-selected set of text characters may include a blank symbol indicative of a pause between speech sounds.

The foregoing, together with other features and embodiments, will become more apparent upon referring to the following specification, claims, and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appended figures:

FIG. 1 illustrates a block diagram that provides an illustration of the hardware components of a computing system, according to some embodiments of the present technology.

FIG. 2 illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to some embodiments of the present technology.

FIG. 3 illustrates a representation of a conceptual model of a communications protocol system, according to some embodiments of the present technology.

FIG. 4 illustrates a communications grid computing system including a variety of control and worker nodes, according to some embodiments of the present technology.

FIG. 5 illustrates a flow chart showing an example process for adjusting a communications grid or a work project in a communications grid after a failure of a node, according to some embodiments of the present technology.

FIG. 6 illustrates a portion of a communications grid computing system including a control node and a worker node, according to some embodiments of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executing a data analysis or processing project, according to some embodiments of the present technology.

FIG. 8 illustrates a block diagram including components of an Event Stream Processing Engine (ESPE), according to embodiments of the present technology.

FIG. 9 illustrates a flow chart showing an example process including operations performed by an event stream processing engine, according to some embodiments of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishing device and multiple event subscribing devices, according to embodiments of the present technology.

FIG. 11 illustrates a flow chart showing an example process of generating and using a machine-learning model according to some aspects.

FIG. 12 illustrates an example machine-learning model based on a neural network.

FIGS. 13A and 13B each illustrate an example embodiment of a processing system.

FIG. 14 illustrates an overview of an example performance of speech-to-text conversion using either of the example embodiments of a processing system of FIGS. 13A-B.

FIGS. 15A, 15B and 15C, together, illustrate an example of employing an APA segmentation technique to derive a candidate set of indications of likely sentence pauses within the speech audio of a speech data set.

FIGS. 16A and 16B, together, illustrate an example of employing a CTC segmentation technique to derive another candidate set of indications of likely sentence pauses within the same speech audio of the same speech data set of FIGS. 15A-C.

FIGS. 17A and 17B, together, illustrate an example of combining the candidate sets of indications of likely speech pauses generated in FIGS. 15A-C and in FIGS. 16A-B to generate a single converged set of indications of likely sentence pauses.

FIGS. 18A, 18B, 18C and 18D, together, illustrate an example of using the converged set of indications of likely sentence pauses generated in FIGS. 17A-B to divide the speech audio into segments, and then using the segments along with acoustic and language models in speech-to-text processing operations.

FIGS. 19A and 19B each illustrate examples of additional speech-to-text processing operations.

FIGS. 20A, 20B, 20C, 20D and 20E, together, illustrate an example logic flow of operations performed within a processing system to perform pre-processing and speech-to-text processing operations.

FIG. 21 illustrates an example logic flow of operations performed within a processing system to perform speech-to-text processing operations.

DETAILED DESCRIPTION

Various embodiments are generally directed to techniques for improving the accuracy of speech-to-text conversion by improving the pre-processing division of speech audio into segments for subsequent speech-to-text processing operations, and/or by improving the relative weighting given to probabilities associated with various portions of speech-related data during speech-to-text processing operations. During pre-processing, as an alternative to the commonplace approach of simply dividing speech audio into equal-length segments without regard to its content, a combination of techniques is used to identify likely sentence pauses to enable the division of the speech audio into speech segments at likely sentence pauses so that the resulting speech segments are more likely to contain the pronunciations of complete sentences. During speech-to-text processing, relative weights of probability values associated with the identification of likely graphemes and associated with the identification of likely n-grams are used in dynamically alterable proportions to identify most likely spoken words.

Turning to the pre-processing operations, as will be familiar to those skilled in the art, many of the components employed in performing many of the processing operations of speech-to-text conversion (e.g., acoustic feature detection, acoustic models, language models, etc.) have capacity limits on how large a portion of speech audio is able to be accepted as input such that speech audio must be divided into segments that fit within such capacity limits. As part of an improved approach to dividing speech audio into such segments, a combination of multiple segmentation techniques is used to provide improved identification of pauses in the speech audio that are likely to be pauses between sentences to enable the division of the speech audio into segments at the midpoints within such likely sentence pauses. By dividing speech audio at midpoints within likely sentence pauses to form the segments, each segment is caused to include a higher proportion of complete pronunciations of whole phonemes, whole words, whole phrases and/or whole sentences, thereby enabling greater accuracy in the performance of subsequent processing operations. Also, with fewer phonemes and/or other speech parts being split across the divides between pairs of adjacent segments, there are fewer fragments of phonemes or other speech parts to potentially cause the errant identification of extra text characters and/or words that aren't actually present. Thus, such improvements in the identification of likely sentence pauses during pre-processing serves to enable corresponding improvements in subsequent processing operations to identify text characters, whole words, phrases and/or sentences.

As will be familiar to those skilled in the art, there are many linguistic characteristics that vary greatly among the wide variety of languages that are spoken around the world. By way of example, the manner in which combinations of tone, volume, generation of vowels versus consonants, etc., are used to form words may different greatly between languages. However, the manner in which the relative lengths of pauses are used to separate sounds within words, to separate words within sentences, and to separate sentences tend to be quite similar. More specifically, the relatively short lengths of pauses between sounds within words tend to arise more out of the time needed to reposition portions of the vocal tract when transitioning from producing one sound to producing another sound amidst pronouncing a word. In contrast, the somewhat longer lengths of pauses between words tend to be dictated more by linguistic rules that provide a mechanism to enable a listener to hear the pronunciations of individual words more easily. Similarly, the still longer lengths of pauses between sentences also tend to be dictated by linguistic rules that provide a mechanism to make clear where the speaking of one sentence ends, and the speaking of the next sentence begins. Thus, the ability to identify pauses and/or to distinguish among pauses within words, pauses between words and/or pauses between sentences may be used by each of the multiple segmentation techniques to identify likely sentence pauses at which speech audio may be divided into segments in a manner that may be independent of the language that is spoken.

In preparation for the performance of the multiple segmentation techniques, the speech audio may be initially divided into equal-length chunks. The full set of chunks of the speech audio may then be provided as an input to each of multiple segmentation techniques, which may be performed, at least partially in parallel, to each independently generate its corresponding data structure specifying its corresponding candidate set of what are deemed to be likely sentence pauses present within the speech audio.

In some embodiments, the multiple segmentation techniques may include an adaptive peak amplitude (APA) segmentation technique in which a peak amplitude is separately determined for each chunk of the speech audio, with a threshold amplitude being derived therefrom that is used to distinguish pauses from speech sounds. More precisely, the peak amplitude that occurs within each chunk is measured, and then a preselected percentile amplitude across all of peak amplitudes of all of the chunks is derived to become a threshold amplitude. With the threshold amplitude so derived, all of the chunks with a peak amplitude above the threshold amplitude are deemed to be speech chunks, while all of the chunks with a peak amplitude below the threshold amplitude are deemed to be pause chunks. In this way, the threshold amplitude used in distinguishing pauses from speech sounds is caused to be adaptive to provide some degree of resiliency in addressing differences in speech audio amplitude and/or in audio noise levels that may thwart the typical use of a fixed threshold amplitude to distinguish between pauses and speech sounds.

Another adaptive mechanism may then be used to distinguish a pause occurring between sentences from other shorter pauses occurring between words or occurring within words, as well as to distinguish from still other shorter pauses that may occur as a result of various anomalies in capturing the speech audio. Starting at the beginning of the speech audio, a window that covers a preselected quantity of temporally adjacent chunks may be shifted across the length of the speech audio, starting with the earliest chunk and proceeding through temporally adjacent chunks toward the temporally latest chunk. More specifically, with the window positioned to begin with the earliest chunk, measurements of the lengths of each identified pause within the window may be taken to identify the longest pause thereamong (i.e., the pause made up of the longest set of consecutive pause chunks). The longest pause that is so identified within the window may then be deemed likely to be a sentence pause. The window may then be shifted away from the earliest chunk and along the speech audio so as to cause the window to now begin with the chunk just after the just-identified likely sentence pause. With the window so repositioned, again, measurements of the lengths of each identified pause within the window may be taken to again identify the longest pause thereamong. Again, the longest pause that is so identified within the window may be deemed likely to be a sentence pause. This may be repeated until the window has been shifted along the entirety of the length of the speech audio to the temporally latest chunk.

Each of the pauses that has been deemed a likely sentence pause may be added to the candidate set of likely sentence pauses derived by the APA segmentation technique. The length of the window may be selected to ensure that there cannot be a distance between any adjacent pair of likely sentence pauses that is greater than a capacity limitation that may be present in subsequent processing. Alternatively or additionally, it may be that instances of any adjacent pair of likely sentence pauses that are closer to each other than a predetermined threshold period of time are not permitted. Wherever such a pair of all-too-close adjacent likely sentence pauses might occur, one or the other may be removed from (or not be permitted to be added to) the candidate set of likely sentence pauses.

Alternatively or additionally, in some embodiments, the multiple segmentation techniques may include the use of a connectionist temporal classification (CTC) segmentation technique in which instances of consecutive blank symbols (sometimes also referred to as “non-alphabetical symbols” or “artificial symbols”) generated by a CTC output of a neural network trained to implement an acoustic model are used to identify likely sentence pauses. A neural network incorporating a CTC output and trained to implement an acoustic model would normally be used to identify likely graphemes, such as text characters representing likely phoneme(s), in speech audio based on various acoustic features that are identified as present therein. In such normal use, the CTC output serves to augment the probabilistic indications of such text characters (graphemes) that are generated by the neural network with blank symbols that serve to identify instances of consecutive occurrences of the same text character (e.g., the pair of “s” characters in the word “chess”), despite the absence of an acoustic feature that would specifically indicate such a situation (e.g., no acoustic feature in the pronunciation of the “s” sound in the word “chess” that indicates that there are two consecutive “s” characters therein). However, it has been observed through experimentation that the CTC output of such a trained neural network may also be useful in identifying sentence pauses, as it has been observed that the CTC output has a tendency to generate relatively long strings of consecutive blank symbols that tend to correspond to where sentence pauses occur.

In using such a trained neural network for the detection of sentence pauses, each chunk is provided to the neural network as an input, and the CTC output for that chunk is monitored for occurrences of strings of consecutive blank symbols, and the length of each such string is compared to a threshold blank string length. Each string of consecutive blank symbols that is at least as long as the threshold blank string length may be deemed to correspond to what is likely a sentence pause. In some embodiments, the threshold blank string length may be derived during training of the neural network to implement the acoustic model, and/or during testing of the results of that training. Portions of speech audio that are known to include pauses between sentences may be provided as input to the neural network and the lengths of the strings of consecutive blank symbols that are output may be monitored to determine what the threshold blank string length should be. Regardless of the exact manner in which the threshold blank string length is arrived at, each of the pauses that has been deemed a likely sentence pause may be added to the candidate set of likely sentence pauses derived by the CTC segmentation technique.

It should be noted that, in some embodiments, the same trained neural network with CTC output that is employed in the CTC segmentation technique during pre-processing may also be employed during the subsequent processing to perform the function for which it was trained. Specifically, that same trained neural network may be used to identify likely text characters from acoustic features detected in the speech audio, including using its CTC output to augment such probabilistic indications of text characters with blank symbols indicative of instances in which there are likely instances of consecutive occurrences of the same text character.

Following the completion of the performances of all of the multiple segmentation techniques, the resulting multiple candidate sets of likely sentence pauses may then be combined in any of a variety of ways to generate a single converged set of likely sentence pauses. In some embodiments, the manner in which multiple candidate sets of likely sentence pauses are combined to derive the converged set of likely sentence pauses may include the use of relative weighting factors that may be dynamically adjusted based on levels of audio noise detected as being present within the speech audio. This may be done in recognition of each of the different segmentation techniques being more or less susceptible than others to audio noise. More specifically, in some embodiments, as the speech audio is being divided into chunks and/or as peak amplitudes are being measured across all of the chunks, a minimum amplitude may also be measured across all of the chunks as part of determining a level of audio noise that is present in the speech audio. The audio noise level may then be used, as a basis for adjusting relative weighting factors assigned to each segmentation technique. Such relative weighting factors may then be used in combining the multiple candidate sets of likely sentence pauses generated by the different segmentation techniques as part of deriving the converged set of likely sentence pauses for each chunk. Regardless of the exact manner in which the converged set of likely sentence pauses is generated from the multiple candidate sets, upon completion of the pre-processing operations, there may be no further use made of the chunks into which the speech audio was initially divided.

Turning to the speech-to-text processing operations, as will be familiar to those skilled in the art, it has become commonplace (at least in speech recognition systems having sufficient processing and storage resources) to employ a two-stage combination of an acoustic model and a language model to identify the words spoken in speech audio. In such speech recognition systems, the acoustic model is typically relied upon to perform a first pass at identifying words that are likely to be the ones that were spoken, and the language model is typically relied upon to perform the next and final pass by refining the identification of such spoken words such that the words identified by the language model are the ones from which a transcript is generated. Such a two-stage use of a combination of acoustic and language models has proven to be significantly more accurate in performing speech recognition than the earlier commonplace practice of applying an acoustic model, alone.

However, while the reduction in errors in speech recognition that have been achieved through such a two-pass use of a combination of acoustic and language models is significant, even this reduced error rate is still high enough as to require further efforts for a number of years to further reduce it. A possible source of this still elevated error rate, at least in some situations, has been such reliance on using a language model to always perform the final pass to provide the final identification of each word spoken in speech audio. It should be remembered that a good language model is usually one that closely models a language as that language is used correctly. Thus, part of the still elevated error rate may arise from the fact that a person may make mistakes in vocabulary and/or syntax when speaking, while the language model may tend to fight against correctly identifying that person's words as actually spoken as it effectively attempts to enforce its model of what that person's words should have been.

As illustrated by at least this one example, there can be situations in which it may be desirable to rely more on an acoustic model, than on a language model, to correctly identify spoken words. It has long been recognized that an acoustic model can be highly accurate in identifying spoken words where the pronunciation of words is of sufficient clarity, and where the acoustic conditions associated with the reception of those spoken words are sufficiently favorable (e.g., sufficiently free of noise). As will be familiar to those skilled in the art, the longstanding practice of reliance on a language model to provide the final identification of words was largely influenced by a need to accommodate less ideal conditions in which the pronunciation of words may not be as clear and/or where the acoustic conditions may not be so favorable. In such situations, gaps may occur in the reception of spoken words, and on many of such occasions, a language model can compensate for such instances of missing acoustic information.

To further improve upon the error rate of such typical two-stage use of a combination of an acoustic model and a language model, some embodiments may dynamically vary the relative weighting assigned to each of the acoustic model and the language model per-word based on the degree of uncertainty in the per-grapheme probability distributions output by the acoustic model for each word. Stated differently, it may be that the probability distributions of graphemes that are output by the acoustic model for a single word are analyzed to derive a corresponding degree of perplexity for each probability distribution. Such a degree of perplexity may serve as an indication of the degree to which a probability distribution presents an indefinite indication of which utterance occurred during a corresponding portion of speech audio. Where the degree of perplexity of probability distributions for graphemes associated with a word are deemed to be lower than a pre-determined threshold, then greater weight may be dynamically assigned to the identification of that word based on those probability distributions such that the acoustic model is relied upon to identify that word. However, where the degree of perplexity of such probability distributions associated with a word are deemed to be higher than a pre-determined threshold, then greater weight may be dynamically assigned to the identification of that word based on the language model.

In some embodiments, both of the acoustic model and the language model may always be utilized in combination for each spoken word, regardless of whether the per-word determination is made to give greater weight to relying more on the acoustic model or the language model to identify a word. Thus, the beam searches associated with such use of a language model implemented with an n-gram corpus may always be performed regardless of such dynamic per-word assignment of relative weighting. In some of such embodiments, it may be that the probability (and/or another measure or statistic) associated with the word identified by the language model is used as an input to the dynamic per-word relative weighting in addition to the degree of perplexity derived for the probability distributions for the corresponding graphemes.

Alternatively, in other embodiments, it may be that the language model is not used to provide any input to the dynamic per-word relative weighting. In such other embodiments, such a situation may provide the opportunity to entirely refrain from consuming processing and or storage resources to perform beam searches associated with using the language model to identify a particular word if the results of the dynamic per-word relative weighting are such that the identification of the word that would be provided by the language model will not be used. In this way, use of the language model may be made contingent on such dynamic per-word relative weighting.

Regardless of the exact manner in which each word spoken in speech audio is identified through use of an acoustic model and/or a language model, the length of transcript(s) that are generated from speech audio may advantageously or adversely affect automated text analyses that may be subsequently performed (e.g., analyses to identify topics, to identify sentiments of topics, and/or to identify other related pieces of speech audio and/or transcripts generated therefrom). From experimentation and observation, it has been found that, generally, many forms of automated text analyses are able to be more successfully used with longer transcripts.

More specifically, it has been found that shorter transcripts tend to cause an overemphasis on the more frequently used words in a language, even after removal of non-content stopwords, with the result that analyses to derive topics and/or other insights of a transcript tend to produce less useful results. To counteract this, in some embodiments, all of the text of speech audio on which speech-to-text processing has been performed may be stored and/or otherwise handled as a single transcript, thereby increasing the likelihood of generating longer transcripts. However, where the speech audio is sufficiently long as to include multiple presentations and/or conversation on unrelated subjects, automated text analyses performed on a single transcript encompassing such lengthy and varied speech audio may also produce less useful results.

Thus, in some embodiments, rules concerning lengths of transcripts and/or acoustic features such as relatively lengthy pauses may be used to bring about the generation of lengths and/or quantities of transcripts for each piece of speech audio that are more amenable to providing useful results from automated text analyses.

In embodiments in which a distributed processing system is used that includes multiple node devices, various operations may be performed in a manner that is distributed across those multiple node devices to improve the efficiency with which those operations are able to be performed. As will be explained in greater detail, such improvements in efficiency may also enable improvements in the handling of data such that greater use may be made of contextual information to provide improved results. By way of example, each of the different segmentation techniques may be performed within a separate one of the node devices, at least partially in parallel, such that a different one of the corresponding candidate set of likely sentence pauses may be independently derived within each such node device.

Also by way of example, multiple instances of the feature detector may be executed across the multiple node devices, and the speech segments may be distributed thereamong to enable speech detection to be performed with multiple ones of the speech segments at least partially in parallel. Further, along with the multiple instances of the feature detector, multiple instances of the acoustic model may be instantiated across the multiple node devices, thereby enabling the feature vectors derived from a speech segment by an instance of the feature detector within a node device to be directly provided to the corresponding instance of the acoustic model within the node device to enable the derivation of the set of probability distributions that correspond to that speech segment.

With general reference to notations and nomenclature used herein, portions of the detailed description that follows may be presented in terms of program procedures executed by a processor of a machine or of multiple networked machines. These procedural descriptions and representations are used by those skilled in the art to most effectively convey the substance of their work to others skilled in the art. A procedure is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. These operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic or optical communications capable of being stored, transferred, combined, compared, and otherwise manipulated. It proves convenient at times, principally for reasons of common usage, to refer to what is communicated as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be noted, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such as adding or comparing, which are commonly associated with mental operations performed by a human operator. However, no such capability of a human operator is necessary, or desirable in most cases, in any of the operations described herein that form part of one or more embodiments. Rather, these operations are machine operations. Useful machines for performing operations of various embodiments include machines selectively activated or configured by a routine stored within that is written in accordance with the teachings herein, and/or include apparatus specially constructed for the required purpose. Various embodiments also relate to apparatus or systems for performing these operations. These apparatus may be specially constructed for the required purpose or may include a general purpose computer. The required structure for a variety of these machines will appear from the description given.

Reference is now made to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the novel embodiments can be practiced without these specific details. In other instances, well known structures and devices are shown in block diagram form in order to facilitate a description thereof. The intention is to cover all modifications, equivalents, and alternatives within the scope of the claims.

Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system and/or a fog computing system.

FIG. 1 is a block diagram that provides an illustration of the hardware components of a data transmission network 100, according to embodiments of the present technology. Data transmission network 100 is a specialized computer system that may be used for processing large amounts of data where a large number of computer processing cycles are required.

Data transmission network 100 may also include computing environment 114. Computing environment 114 may be a specialized computer or other machine that processes the data received within the data transmission network 100. Data transmission network 100 also includes one or more network devices 102. Network devices 102 may include client devices that attempt to communicate with computing environment 114. For example, network devices 102 may send data to the computing environment 114 to be processed, may send signals to the computing environment 114 to control different aspects of the computing environment or the data it is processing, among other reasons. Network devices 102 may interact with the computing environment 114 through a number of ways, such as, for example, over one or more networks 108. As shown in FIG. 1, computing environment 114 may include one or more other systems. For example, computing environment 114 may include a database system 118 and/or a communications grid 120.

In other embodiments, network devices may provide a large amount of data, either all at once or streaming over a period of time (e.g., using event stream processing (ESP), described further with respect to FIGS. 8-10), to the computing environment 114 via networks 108. For example, network devices 102 may include network computers, sensors, databases, or other devices that may transmit or otherwise provide data to computing environment 114. For example, network devices may include local area network devices, such as routers, hubs, switches, or other computer networking devices. These devices may provide a variety of stored or generated data, such as network data or data specific to the network devices themselves. Network devices may also include sensors that monitor their environment or other devices to collect data regarding that environment or those devices, and such network devices may provide data they collect over time. Network devices may also include devices within the internet of things, such as devices within a home automation network. Some of these devices may be referred to as edge devices, and may involve edge computing circuitry. Data may be transmitted by network devices directly to computing environment 114 or to network-attached data stores, such as network-attached data stores 110 for storage so that the data may be retrieved later by the computing environment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or more network-attached data stores 110. Network-attached data stores 110 are used to store data to be processed by the computing environment 114 as well as any intermediate or final data generated by the computing system in non-volatile memory. However in certain embodiments, the configuration of the computing environment 114 allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory (e.g., disk). This can be useful in certain situations, such as when the computing environment 114 receives ad hoc queries from a user and when responses, which are generated by processing large amounts of data, need to be generated on-the-fly. In this non-limiting situation, the computing environment 114 may be configured to retain the processed information within memory so that responses can be generated for the user at different levels of detail as well as allow a user to interactively query against this information.

Network-attached data stores may store a variety of different types of data organized in a variety of different ways and from a variety of different sources. For example, network-attached data storage may include storage other than primary storage located within computing environment 114 that is directly accessible by processors located therein. Network-attached data storage may include secondary, tertiary or auxiliary storage, such as large hard drives, servers, virtual memory, among other types. Storage devices may include portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals. Examples of a non-transitory medium may include, for example, a magnetic disk or tape, optical storage media such as compact disk or digital versatile disk, flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, among others. Furthermore, the data stores may hold a variety of different types of data. For example, network-attached data stores 110 may hold unstructured (e.g., raw) data, such as manufacturing data (e.g., a database containing records identifying products being manufactured with parameter data for each product, such as colors and models) or product sales databases (e.g., a database containing individual data records identifying details of individual product sales).

The unstructured data may be presented to the computing environment 114 in different forms such as a flat file or a conglomerate of data records, and may have data values and accompanying time stamps. The computing environment 114 may be used to analyze the unstructured data in a variety of ways to determine the best way to structure (e.g., hierarchically) that data, such that the structured data is tailored to a type of further analysis that a user wishes to perform on the data. For example, after being processed, the unstructured time stamped data may be aggregated by time (e.g., into daily time period units) to generate time series data and/or structured hierarchically according to one or more dimensions (e.g., parameters, attributes, and/or variables). For example, data may be stored in a hierarchical data structure, such as a ROLAP OR MOLAP database, or may be stored in another tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms 106. Computing environment 114 may route select communications or data to the one or more sever farms 106 or one or more servers within the server farms. Server farms 106 can be configured to provide information in a predetermined manner. For example, server farms 106 may access data to transmit in response to a communication. Server farms 106 may be separately housed from each other device within data transmission network 100, such as computing environment 114, and/or may be part of a device or system.

Server farms 106 may host a variety of different types of data processing as part of data transmission network 100. Server farms 106 may receive a variety of different data from network devices, from computing environment 114, from cloud network 116, or from other sources. The data may have been obtained or collected from one or more sensors, as inputs from a control database, or may have been received as inputs from an external system or device. Server farms 106 may assist in processing the data by turning raw data into processed data based on one or more rules implemented by the server farms. For example, sensor data may be analyzed to determine changes in an environment over time or in real-time.

Data transmission network 100 may also include one or more cloud networks 116. Cloud network 116 may include a cloud infrastructure system that provides cloud services. In certain embodiments, services provided by the cloud network 116 may include a host of services that are made available to users of the cloud infrastructure system on demand. Cloud network 116 is shown in FIG. 1 as being connected to computing environment 114 (and therefore having computing environment 114 as its client or user), but cloud network 116 may be connected to or utilized by any of the devices in FIG. 1. Services provided by the cloud network can dynamically scale to meet the needs of its users. The cloud network 116 may include one or more computers, servers, and/or systems. In some embodiments, the computers, servers, and/or systems that make up the cloud network 116 are different from the user's own on-premises computers, servers, and/or systems. For example, the cloud network 116 may host an application, and a user may, via a communication network such as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a single device, it will be appreciated that multiple devices may instead be used. For example, a set of network devices can be used to transmit various communications from a single user, or remote server 140 may include a server stack. As another example, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., between client devices, between servers 106 and computing environment 114 or between a server and a device) may occur over one or more networks 108. Networks 108 may include one or more of a variety of different types of networks, including a wireless network, a wired network, or a combination of a wired and wireless network. Examples of suitable networks include the Internet, a personal area network, a local area network (LAN), a wide area network (WAN), or a wireless local area network (WLAN). A wireless network may include a wireless interface or combination of wireless interfaces. As an example, a network in the one or more networks 108 may include a short-range communication channel, such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energy communication channel. A wired network may include a wired interface. The wired and/or wireless networks may be implemented using routers, access points, bridges, gateways, or the like, to connect devices in the network 114, as will be further described with respect to FIG. 2. The one or more networks 108 can be incorporated entirely within or can include an intranet, an extranet, or a combination thereof. In one embodiment, communications between two or more systems and/or devices can be achieved by a secure communications protocol, such as secure sockets layer (SSL) or transport layer security (TLS). In addition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things (e.g., machines, devices, phones, sensors) can be connected to networks and the data from these things can be collected and processed within the things and/or external to the things. For example, the IoT can include sensors in many different devices, and high value analytics can be applied to identify hidden relationships and drive increased efficiencies. This can apply to both big data analytics and real-time (e.g., ESP) analytics. This will be described further below with respect to FIG. 2.

As noted, computing environment 114 may include a communications grid 120 and a transmission network database system 118. Communications grid 120 may be a grid-based computing system for processing large amounts of data. The transmission network database system 118 may be for managing, storing, and retrieving large amounts of data that are distributed to and stored in the one or more network-attached data stores 110 or other data stores that reside at different locations within the transmission network database system 118. The compute nodes in the grid-based computing system 120 and the transmission network database system 118 may share the same processor hardware, such as processors that are located within computing environment 114.

FIG. 2 illustrates an example network including an example set of devices communicating with each other over an exchange system and via a network, according to embodiments of the present technology. As noted, each communication within data transmission network 100 may occur over one or more networks. System 200 includes a network device 204 configured to communicate with a variety of types of client devices, for example client devices 230, over a variety of types of communication channels.

As shown in FIG. 2, network device 204 can transmit a communication over a network (e.g., a cellular network via a base station 210). The communication can be routed to another network device, such as network devices 205-209, via base station 210. The communication can also be routed to computing environment 214 via base station 210. For example, network device 204 may collect data either from its surrounding environment or from other network devices (such as network devices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone, laptop computer, tablet computer, temperature sensor, motion sensor, and audio sensor respectively, the network devices may be or include sensors that are sensitive to detecting aspects of their environment. For example, the network devices may include sensors such as water sensors, power sensors, electrical current sensors, chemical sensors, optical sensors, pressure sensors, geographic or position sensors (e.g., GPS), velocity sensors, acceleration sensors, flow rate sensors, among others. Examples of characteristics that may be sensed include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, electrical current, among others. The sensors may be mounted to various components used as part of a variety of different types of systems (e.g., an oil drilling operation). The network devices may detect and record data related to the environment that it monitors, and transmit that data to computing environment 214.

As noted, one type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes an oil drilling system. For example, the one or more drilling operation sensors may include surface sensors that measure a hook load, a fluid rate, a temperature and a density in and out of the wellbore, a standpipe pressure, a surface torque, a rotation speed of a drill pipe, a rate of penetration, a mechanical specific energy, etc. and downhole sensors that measure a rotation speed of a bit, fluid densities, downhole torque, downhole vibration (axial, tangential, lateral), a weight applied at a drill bit, an annular pressure, a differential pressure, an azimuth, an inclination, a dog leg severity, a measured depth, a vertical depth, a downhole temperature, etc. Besides the raw data collected directly by the sensors, other data may include parameters either developed by the sensors or assigned to the system by a client or other controlling device. For example, one or more drilling operation control parameters may control settings such as a mud motor speed to flow ratio, a bit diameter, a predicted formation top, seismic data, weather data, etc. Other data may be generated using physical models such as an earth model, a weather model, a seismic model, a bottom hole assembly model, a well plan model, an annular friction model, etc. In addition to sensor and control settings, predicted outputs, of for example, the rate of penetration, mechanical specific energy, hook load, flow in fluid rate, flow out fluid rate, pump pressure, surface torque, rotation speed of the drill pipe, annular pressure, annular friction pressure, annular temperature, equivalent circulating density, etc. may also be stored in the data warehouse.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a home automation or similar automated network in a different environment, such as an office space, school, public space, sports venue, or a variety of other locations. Network devices in such an automated network may include network devices that allow a user to access, control, and/or configure various home appliances located within the user's home (e.g., a television, radio, light, fan, humidifier, sensor, microwave, iron, and/or the like), or outside of the user's home (e.g., exterior motion sensors, exterior lighting, garage door openers, sprinkler systems, or the like). For example, network device 102 may include a home automation switch that may be coupled with a home appliance. In another embodiment, a network device can allow a user to access, control, and/or configure devices, such as office-related devices (e.g., copy machine, printer, or fax machine), audio and/or video related devices (e.g., a receiver, a speaker, a projector, a DVD player, or a television), media-playback devices (e.g., a compact disc player, a CD player, or the like), computing devices (e.g., a home computer, a laptop computer, a tablet, a personal digital assistant (PDA), a computing device, or a wearable device), lighting devices (e.g., a lamp or recessed lighting), devices associated with a security system, devices associated with an alarm system, devices that can be operated in an automobile (e.g., radio devices, navigation devices), and/or the like. Data may be collected from such various sensors in raw form, or data may be processed by the sensors to create parameters or other data either developed by the sensors based on the raw data or assigned to the system by a client or other controlling device.

In another example, another type of system that may include various sensors that collect data to be processed and/or transmitted to a computing environment according to certain embodiments includes a power or energy grid. A variety of different network devices may be included in an energy grid, such as various devices within one or more power plants, energy farms (e.g., wind farm, solar farm, among others) energy storage facilities, factories, homes and businesses of consumers, among others. One or more of such devices may include one or more sensors that detect energy gain or loss, electrical input or output or loss, and a variety of other efficiencies. These sensors may collect data to inform users of how the energy grid, and individual devices within the grid, may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collects before transmitting the data to the computing environment 114, or before deciding whether to transmit data to the computing environment 114. For example, network devices may determine whether data collected meets certain rules, for example by comparing data or values calculated from the data and comparing that data to one or more thresholds. The network device may use this data and/or comparisons to determine if the data should be transmitted to the computing environment 214 for further use or processing.

Computing environment 214 may include machines 220 and 240. Although computing environment 214 is shown in FIG. 2 as having two machines, 220 and 240, computing environment 214 may have only one machine or may have more than two machines. The machines that make up computing environment 214 may include specialized computers, servers, or other machines that are configured to individually and/or collectively process large amounts of data. The computing environment 214 may also include storage devices that include one or more databases of structured data, such as data organized in one or more hierarchies, or unstructured data. The databases may communicate with the processing devices within computing environment 214 to distribute data to them. Since network devices may transmit data to computing environment 214, that data may be received by the computing environment 214 and subsequently stored within those storage devices. Data used by computing environment 214 may also be stored in data stores 235, which may also be a part of or connected to computing environment 214.

Computing environment 214 can communicate with various devices via one or more routers 225 or other inter-network or intra-network connection components. For example, computing environment 214 may communicate with devices 230 via one or more routers 225. Computing environment 214 may collect, analyze and/or store data from or pertaining to communications, client device operations, client rules, and/or user-associated actions stored at one or more data stores 235. Such data may influence communication routing to the devices within computing environment 214, how data is stored or processed within computing environment 214, among other actions.

Notably, various other devices can further be used to influence communication routing and/or processing between devices within computing environment 214 and with devices outside of computing environment 214. For example, as shown in FIG. 2, computing environment 214 may include a web server 240. Thus, computing environment 214 can retrieve data of interest, such as client information (e.g., product information, client rules, etc.), technical product details, news, current or predicted weather, and so on.

In addition to computing environment 214 collecting data (e.g., as received from network devices, such as sensors, and client devices or other sources) to be processed as part of a big data analytics project, it may also receive data in real time as part of a streaming analytics environment. As noted, data may be collected using a variety of sources as communicated via different kinds of networks or locally. Such data may be received on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. Devices within computing environment 214 may also perform pre-analysis on data it receives to determine if the data received should be processed as part of an ongoing project. The data received and collected by computing environment 214, no matter what the source or method or timing of receipt, may be processed over a period of time for a client to determine results data based on the client's needs and rules.

FIG. 3 illustrates a representation of a conceptual model of a communications protocol system, according to embodiments of the present technology. More specifically, FIG. 3 identifies operation of a computing environment in an Open Systems Interaction model that corresponds to various connection components. The model 300 shows, for example, how a computing environment, such as computing environment 314 (or computing environment 214 in FIG. 2) may communicate with other devices in its network, and control how communications between the computing environment and other devices are executed and under what conditions.

The model can include layers 301-307. The layers are arranged in a stack. Each layer in the stack serves the layer one level higher than it (except for the application layer, which is the highest layer), and is served by the layer one level below it (except for the physical layer, which is the lowest layer). The physical layer is the lowest layer because it receives and transmits raw bites of data, and is the farthest layer from the user in a communications system. On the other hand, the application layer is the highest layer because it interacts directly with a software application.

As noted, the model includes a physical layer 301. Physical layer 301 represents physical communication, and can define parameters of that physical communication. For example, such physical communication may come in the form of electrical, optical, or electromagnetic signals. Physical layer 301 also defines protocols that may control communications within a data transmission network.

Link layer 302 defines links and mechanisms used to transmit (i.e., move) data across a network. The link layer 302 manages node-to-node communications, such as within a grid computing environment. Link layer 302 can detect and correct errors (e.g., transmission errors in the physical layer 301). Link layer 302 can also include a media access control (MAC) layer and logical link control (LLC) layer.

Network layer 303 defines the protocol for routing within a network. In other words, the network layer coordinates transferring data across nodes in a same network (e.g., such as a grid computing environment). Network layer 303 can also define the processes used to structure local addressing within the network.

Transport layer 304 can manage the transmission of data and the quality of the transmission and/or receipt of that data. Transport layer 304 can provide a protocol for transferring data, such as, for example, a Transmission Control Protocol (TCP). Transport layer 304 can assemble and disassemble data frames for transmission. The transport layer can also detect transmission errors occurring in the layers below it.

Session layer 305 can establish, maintain, and manage communication connections between devices on a network. In other words, the session layer controls the dialogues or nature of communications between network devices on the network. The session layer may also establish checkpointing, adjournment, termination, and restart procedures.

Presentation layer 306 can provide translation for communications between the application and network layers. In other words, this layer may encrypt, decrypt and/or format data based on data types and/or encodings known to be accepted by an application or network layer.

Application layer 307 interacts directly with software applications and end users, and manages communications between them. Application layer 307 can identify destinations, local resource states or availability and/or communication content or formatting using the applications.

Intra-network connection components 321 and 322 are shown to operate in lower levels, such as physical layer 301 and link layer 302, respectively. For example, a hub can operate in the physical layer, a switch can operate in the link layer, and a router can operate in the network layer. Inter-network connection components 323 and 328 are shown to operate on higher levels, such as layers 303-307. For example, routers can operate in the network layer and network devices can operate in the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operate on, in various embodiments, one, more, all or any of the various layers. For example, computing environment 314 can interact with a hub (e.g., via the link layer) so as to adjust which devices the hub communicates with. The physical layer may be served by the link layer, so it may implement such data from the link layer. For example, the computing environment 314 may control which devices it will receive data from. For example, if the computing environment 314 knows that a certain network device has turned off, broken, or otherwise become unavailable or unreliable, the computing environment 314 may instruct the hub to prevent any data from being transmitted to the computing environment 314 from that network device. Such a process may be beneficial to avoid receiving data that is inaccurate or that has been influenced by an uncontrolled environment. As another example, computing environment 314 can communicate with a bridge, switch, router or gateway and influence which device within the system (e.g., system 200) the component selects as a destination. In some embodiments, computing environment 314 can interact with various layers by exchanging communications with equipment operating on a particular layer by routing or modifying existing communications. In another embodiment, such as in a grid computing environment, a node may determine how data within the environment should be routed (e.g., which node should receive certain data) based on certain parameters or information provided by other layers within the model.

As noted, the computing environment 314 may be a part of a communications grid environment, the communications of which may be implemented as shown in the protocol of FIG. 3. For example, referring back to FIG. 2, one or more of machines 220 and 240 may be part of a communications grid computing environment. A gridded computing environment may be employed in a distributed system with non-interactive workloads where data resides in memory on the machines, or compute nodes. In such an environment, analytic code, instead of a database management system, controls the processing performed by the nodes. Data is co-located by pre-distributing it to the grid nodes, and the analytic code on each node loads the local data into memory. Each node may be assigned a particular task such as a portion of a processing project, or to organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 including a variety of control and worker nodes, according to embodiments of the present technology. Communications grid computing system 400 includes three control nodes and one or more worker nodes. Communications grid computing system 400 includes control nodes 402, 404, and 406. The control nodes are communicatively connected via communication paths 451, 453, and 455. Therefore, the control nodes may transmit information (e.g., related to the communications grid or notifications), to and receive information from each other. Although communications grid computing system 400 is shown in FIG. 4 as including three control nodes, the communications grid may include more or less than three control nodes.

Communications grid computing system (or just “communications grid”) 400 also includes one or more worker nodes. Shown in FIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six worker nodes, a communications grid according to embodiments of the present technology may include more or less than six worker nodes. The number of worker nodes included in a communications grid may be dependent upon how large the project or data set is being processed by the communications grid, the capacity of each worker node, the time designated for the communications grid to complete the project, among others. Each worker node within the communications grid 400 may be connected (wired or wirelessly, and directly or indirectly) to control nodes 402-406. Therefore, each worker node may receive information from the control nodes (e.g., an instruction to perform work on a project) and may transmit information to the control nodes (e.g., a result from work performed on a project). Furthermore, worker nodes may communicate with each other (either directly or indirectly). For example, worker nodes may transmit data between each other related to a job being performed or an individual task within a job being performed by that worker node. However, in certain embodiments, worker nodes may not, for example, be connected (communicatively or otherwise) to certain other worker nodes. In an embodiment, worker nodes may only be able to communicate with the control node that controls it, and may not be able to communicate with other worker nodes in the communications grid, whether they are other worker nodes controlled by the control node that controls the worker node, or worker nodes that are controlled by other control nodes in the communications grid.

A control node may connect with an external device with which the control node may communicate (e.g., a grid user, such as a server or computer, may connect to a controller of the grid). For example, a server or computer may connect to control nodes and may transmit a project or job to the node. The project may include a data set. The data set may be of any size. Once the control node receives such a project including a large data set, the control node may distribute the data set or projects related to the data set to be performed by worker nodes. Alternatively, for a project including a large data set, the data set may be received or stored by a machine other than a control node (e.g., a HADOOP® standard-compliant data node employing the HADOOP® distributed file system, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, coordinate the worker nodes, among other responsibilities. Worker nodes may accept work requests from a control node and provide the control node with results of the work performed by the worker node. A grid may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary control node that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or a controller of the grid) it may be assigned to a set of nodes. After the nodes are assigned to a project, a data structure (i.e., a communicator) may be created. The communicator may be used by the project for information to be shared between the project code running on each node. A communication handle may be created on each node. A handle, for example, is a reference to the communicator that is valid within a single process on a single node, and the handle may be used when requesting communications between nodes.

A control node, such as control node 402, may be designated as the primary control node. A server, computer or other external device may connect to the primary control node. Once the control node receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on communications grid 400, primary control node 402 controls the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time. For example, a worker node may perform analysis on a portion of data that is already local (e.g., stored on) the worker node. The primary control node also coordinates and processes the results of the work performed by each worker node after each worker node executes and completes its job. For example, the primary control node may receive a result from one or more worker nodes, and the control node may organize (e.g., collect and assemble) the results received and compile them to produce a complete result for the project received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may be assigned as backup control nodes for the project. In an embodiment, backup control nodes may not control any portion of the project. Instead, backup control nodes may serve as a backup for the primary control node and take over as primary control node if the primary control node were to fail. If a communications grid were to include only a single control node, and the control node were to fail (e.g., the control node is shut off or breaks) then the communications grid as a whole may fail and any project or job being run on the communications grid may fail and may not complete. While the project may be run again, such a failure may cause a delay (severe delay in some cases, such as overnight delay) in completion of the project. Therefore, a grid with multiple control nodes, including a backup control node, may be beneficial.

To add another node or machine to the grid, the primary control node may open a pair of listening sockets, for example. A socket may be used to accept work requests from clients, and the second socket may be used to accept connections from other grid nodes. The primary control node may be provided with a list of other nodes (e.g., other machines, computers, servers) that will participate in the grid, and the role that each node will fill in the grid. Upon startup of the primary control node (e.g., the first node on the grid), the primary control node may use a network protocol to start the server process on every other node in the grid. Command line parameters, for example, may inform each node of one or more pieces of information, such as: the role that the node will have in the grid, the host name of the primary control node, the port number on which the primary control node is accepting connections from peer nodes, among others. The information may also be provided in a configuration file, transmitted over a secure shell tunnel, recovered from a configuration server, among others. While the other machines in the grid may not initially know about the configuration of the grid, that information may also be sent to each other node by the primary control node. Updates of the grid information may also be subsequently sent to those nodes.

For any control node other than the primary control node added to the grid, the control node may open three sockets. The first socket may accept work requests from clients, the second socket may accept connections from other grid members, and the third socket may connect (e.g., permanently) to the primary control node. When a control node (e.g., primary control node) receives a connection from another control node, it first checks to see if the peer node is in the list of configured nodes in the grid. If it is not on the list, the control node may clear the connection. If it is on the list, it may then attempt to authenticate the connection. If authentication is successful, the authenticating node may transmit information to its peer, such as the port number on which a node is listening for connections, the host name of the node, information about how to authenticate the node, among other information. When a node, such as the new control node, receives information about another active node, it will check to see if it already has a connection to that other node. If it does not have a connection to that node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to the primary control node and any other control nodes on the grid. After establishing the connection, it may authenticate itself to the grid (e.g., any control nodes, including both primary and backup, or a server or user controlling the grid). After successful authentication, the worker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is powered on or connected to an existing node on the grid or both), the node is assigned (e.g., by an operating system of the grid) a universally unique identifier (UUID). This unique identifier may help other nodes and external entities (devices, users, etc.) to identify the node and distinguish it from other nodes. When a node is connected to the grid, the node may share its unique identifier with the other nodes in the grid. Since each node may share its unique identifier, each node may know the unique identifier of every other node on the grid. Unique identifiers may also designate a hierarchy of each of the nodes (e.g., backup control nodes) within the grid. For example, the unique identifiers of each of the backup control nodes may be stored in a list of backup control nodes to indicate an order in which the backup control nodes will take over for a failed primary control node to become a new primary control node. However, a hierarchy of nodes may also be determined using methods other than using the unique identifiers of the nodes. For example, the hierarchy may be predetermined, or may be assigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from any control node). Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.

Primary control node 402 may, for example, transmit one or more communications to backup control nodes 404 and 406 (and, for example, to other control or worker nodes within the communications grid). Such communications may sent periodically, at fixed time intervals, between known fixed stages of the project's execution, among other protocols. The communications transmitted by primary control node 402 may be of varied types and may include a variety of types of information. For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the communications grid. The backup control nodes may receive and store the backup data received from the primary control node. The backup control nodes may transmit a request for such a snapshot (or other information) from the primary control node, or the primary control node may send such information periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch. If the primary control node fails, the backup control node that will take over as primary control node may retrieve the most recent version of the snapshot received from the primary control node and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that the primary control node has failed. In one example of such a method, the primary control node may transmit (e.g., periodically) a communication to the backup control node that indicates that the primary control node is working and has not failed, such as a heartbeat communication. The backup control node may determine that the primary control node has failed if the backup control node has not received a heartbeat communication for a certain predetermined period of time. Alternatively, a backup control node may also receive a communication from the primary control node itself (before it failed) or from a worker node that the primary control node has failed, for example because the primary control node has failed to communicate with the worker node.

Different methods may be performed to determine which backup control node of a set of backup control nodes (e.g., backup control nodes 404 and 406) will take over for failed primary control node 402 and become the new primary control node. For example, the new primary control node may be chosen based on a ranking or “hierarchy” of backup control nodes based on their unique identifiers. In an alternative embodiment, a backup control node may be assigned to be the new primary control node by another device in the communications grid or from an external device (e.g., a system infrastructure or an end user, such as a server or computer, controlling the communications grid). In another alternative embodiment, the backup control node that takes over as the new primary control node may be designated based on bandwidth or other statistics about the communications grid.

A worker node within the communications grid may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes. In an alternative embodiment, the primary control node may transmit a communication to each of the operable worker nodes still on the communications grid that each of the worker nodes should purposefully fail also. After each of the worker nodes fail, they may each retrieve their most recent saved checkpoint of their status and restart the project from that checkpoint to minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process 500 for adjusting a communications grid or a work project in a communications grid after a failure of a node, according to embodiments of the present technology. The process may include, for example, receiving grid status information including a project status of a portion of a project being executed by a node in the communications grid, as described in operation 502. For example, a control node (e.g., a backup control node connected to a primary control node and a worker node on a communications grid) may receive grid status information, where the grid status information includes a project status of the primary control node or a project status of the worker node. The project status of the primary control node and the project status of the worker node may include a status of one or more portions of a project being executed by the primary and worker nodes in the communications grid. The process may also include storing the grid status information, as described in operation 504. For example, a control node (e.g., a backup control node) may store the received grid status information locally within the control node. Alternatively, the grid status information may be sent to another device for storage where the control node may have access to the information.

The process may also include receiving a failure communication corresponding to a node in the communications grid in operation 506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. In an alternative embodiment, a node may receive a failure that a worker node has failed, prompting a control node to reassign the work being performed by the worker node. The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation 508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.

The process may also include receiving updated grid status information based on the reassignment, as described in operation 510, and transmitting a set of instructions based on the updated grid status information to one or more nodes in the communications grid, as described in operation 512. The updated grid status information may include an updated project status of the primary control node or an updated project status of the worker node. The updated information may be transmitted to the other nodes in the grid to update their stale stored information.

FIG. 6 illustrates a portion of a communications grid computing system 600 including a control node and a worker node, according to embodiments of the present technology. Communications grid 600 computing system includes one control node (control node 602) and one worker node (worker node 610) for purposes of illustration, but may include more worker and/or control nodes. The control node 602 is communicatively connected to worker node 610 via communication path 650. Therefore, control node 602 may transmit information (e.g., related to the communications grid or notifications), to and receive information from worker node 610 via path 650.

Similar to in FIG. 4, communications grid computing system (or just “communications grid”) 600 includes data processing nodes (control node 602 and worker node 610). Nodes 602 and 610 include multi-core data processors. Each node 602 and 610 includes a grid-enabled software component (GESC) 620 that executes on the data processor associated with that node and interfaces with buffer memory 622 also associated with that node. Each node 602 and 610 includes a database management software (DBMS) 628 that executes on a database server (not shown) at control node 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar to network-attached data stores 110 in FIG. 1 and data stores 235 in FIG. 2, are used to store data to be processed by the nodes in the computing environment. Data stores 624 may also store any intermediate or final data generated by the computing system after being processed, for example in non-volatile memory. However in certain embodiments, the configuration of the grid computing environment allows its operations to be performed such that intermediate and final data results can be stored solely in volatile memory (e.g., RAM), without a requirement that intermediate or final data results be stored to non-volatile types of memory. Storing such data in volatile memory may be useful in certain situations, such as when the grid receives queries (e.g., ad hoc) from a client and when responses, which are generated by processing large amounts of data, need to be generated quickly or on-the-fly. In such a situation, the grid may be configured to retain the data within memory so that responses can be generated at different levels of detail and so that a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDF provides a mechanism for the DBMS 628 to transfer data to or receive data from the database stored in the data stores 624 that are managed by the DBMS. For example, UDF 626 can be invoked by the DBMS to provide data to the GESC for processing. The UDF 626 may establish a socket connection (not shown) with the GESC to transfer the data. Alternatively, the UDF 626 can transfer data to the GESC by writing data to shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network, such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI). Each GESC 620 can engage in point-to-point communication with the GESC at another node or in collective communication with multiple GESCs via the network. The GESC 620 at each node may contain identical (or nearly identical) software instructions. Each node may be capable of operating as either a control node or a worker node. The GESC at the control node 602 can communicate, over a communication path 652, with a client deice 630. More specifically, control node 602 may communicate with client application 632 hosted by the client device 630 to receive queries and to respond to those queries after processing large amounts of data.

DBMS 628 may control the creation, maintenance, and use of database or data structure (not shown) within a nodes 602 or 610. The database may organize data stored in data stores 624. The DBMS 628 at control node 602 may accept requests for data and transfer the appropriate data for the request. With such a process, collections of data may be distributed across multiple physical locations. In this example, each node 602 and 610 stores a portion of the total data managed by the management system in its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against data loss using replication techniques. Replication includes providing a backup copy of data stored on one node on one or more other nodes. Therefore, if one node fails, the data from the failed node can be recovered from a replicated copy residing at another node. However, as described herein with respect to FIG. 4, data or status information for each node in the communications grid may also be shared with each node on the grid.

FIG. 7 illustrates a flow chart showing an example method 700 for executing a project within a grid computing system, according to embodiments of the present technology. As described with respect to FIG. 6, the GESC at the control node may transmit data with a client device (e.g., client device 630) to receive queries for executing a project and to respond to those queries after large amounts of data have been processed. The query may be transmitted to the control node, where the query may include a request for executing a project, as described in operation 702. The query can contain instructions on the type of data analysis to be performed in the project and whether the project should be executed using the grid-based computing environment, as shown in operation 704.

To initiate the project, the control node may determine if the query requests use of the grid-based computing environment to execute the project. If the determination is no, then the control node initiates execution of the project in a solo environment (e.g., at the control node), as described in operation 710. If the determination is yes, the control node may initiate execution of the project in the grid-based computing environment, as described in operation 706. In such a situation, the request may include a requested configuration of the grid. For example, the request may include a number of control nodes and a number of worker nodes to be used in the grid when executing the project. After the project has been completed, the control node may transmit results of the analysis yielded by the grid, as described in operation 708. Whether the project is executed in a solo or grid-based environment, the control node provides the results of the project, as described in operation 712.

As noted with respect to FIG. 2, the computing environments described herein may collect data (e.g., as received from network devices, such as sensors, such as network devices 204-209 in FIG. 2, and client devices or other sources) to be processed as part of a data analytics project, and data may be received in real time as part of a streaming analytics environment (e.g., ESP). Data may be collected using a variety of sources as communicated via different kinds of networks or locally, such as on a real-time streaming basis. For example, network devices may receive data periodically from network device sensors as the sensors continuously sense, monitor and track changes in their environments. More specifically, an increasing number of distributed applications develop or produce continuously flowing data from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. An event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities should receive the data. Client or other devices may also subscribe to the ESPE or other devices processing ESP data so that they can receive data after processing, based on for example the entities determined by the processing engine. For example, client devices 230 in FIG. 2 may subscribe to the ESPE in computing environment 214. In another example, event subscription devices 1024 a-c, described further with respect to FIG. 10, may also subscribe to the ESPE. The ESPE may determine or define how input data or event streams from network devices or other publishers (e.g., network devices 204-209 in FIG. 2) are transformed into meaningful output data to be consumed by subscribers, such as for example client devices 230 in FIG. 2.

FIG. 8 illustrates a block diagram including components of an Event Stream Processing Engine (ESPE), according to embodiments of the present technology. ESPE 800 may include one or more projects 802. A project may be described as a second-level container in an engine model managed by ESPE 800 where a thread pool size for the project may be defined by a user. Each project of the one or more projects 802 may include one or more continuous queries 804 that contain data flows, which are data transformations of incoming event streams. The one or more continuous queries 804 may include one or more source windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related to certain events, such as events or other data sensed by one or more network devices. The ESPE may perform operations associated with processing data created by the one or more devices. For example, the ESPE may receive data from the one or more network devices 204-209 shown in FIG. 2. As noted, the network devices may include sensors that sense different aspects of their environments, and may collect data over time based on those sensed observations. For example, the ESPE may be implemented within one or more of machines 220 and 240 shown in FIG. 2. The ESPE may be implemented within such a machine by an ESP application. An ESP application may embed an ESPE with its own dedicated thread pool or pools into its application space where the main application thread can do application-specific work and the ESPE processes event streams at least by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that manages the resources of the one or more projects 802. In an illustrative embodiment, for example, there may be only one ESPE 800 for each instance of the ESP application, and ESPE 800 may have a unique engine name. Additionally, the one or more projects 802 may each have unique project names, and each query may have a unique continuous query name and begin with a uniquely named source window of the one or more source windows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windows for event stream manipulation and transformation. A window in the context of event stream manipulation and transformation is a processing node in an event stream processing model. A window in a continuous query can perform aggregations, computations, pattern-matching, and other operations on data flowing through the window. A continuous query may be described as a directed graph of source, relational, pattern matching, and procedural windows. The one or more source windows 806 and the one or more derived windows 808 represent continuously executing queries that generate updates to a query result set as new event blocks stream through ESPE 800. A directed graph, for example, is a set of nodes connected by edges, where the edges have a direction associated with them.

An event object may be described as a packet of data accessible as a collection of fields, with at least one of the fields defined as a key or unique identifier (ID). The event object may be created using a variety of formats including binary, alphanumeric, XML, etc. Each event object may include one or more fields designated as a primary identifier (ID) for the event so ESPE 800 can support operation codes (opcodes) for events including insert, update, upsert, and delete. Upsert opcodes update the event if the key field already exists; otherwise, the event is inserted. For illustration, an event object may be a packed binary representation of a set of field values and include both metadata and field data associated with an event. The metadata may include an opcode indicating if the event represents an insert, update, delete, or upsert, a set of flags indicating if the event is a normal, partial-update, or a retention generated event from retention policy management, and a set of microsecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of event objects. An event stream may be described as a flow of event block objects. A continuous query of the one or more continuous queries 804 transforms a source event stream made up of streaming event block objects published into ESPE 800 into one or more output event streams using the one or more source windows 806 and the one or more derived windows 808. A continuous query can also be thought of as data flow modeling.

The one or more source windows 806 are at the top of the directed graph and have no windows feeding into them. Event streams are published into the one or more source windows 806, and from there, the event streams may be directed to the next set of connected windows as defined by the directed graph. The one or more derived windows 808 are all instantiated windows that are not source windows and that have other windows streaming events into them. The one or more derived windows 808 may perform computations or transformations on the incoming event streams. The one or more derived windows 808 transform event streams based on the window type (that is operators such as join, filter, compute, aggregate, copy, pattern match, procedural, union, etc.) and window settings. As event streams are published into ESPE 800, they are continuously queried, and the resulting sets of derived windows in these queries are continuously updated.

FIG. 9 illustrates a flow chart showing an example process including operations performed by an event stream processing engine, according to some embodiments of the present technology. As noted, the ESPE 800 (or an associated ESP application) defines how input event streams are transformed into meaningful output event streams. More specifically, the ESP application may define how input event streams from publishers (e.g., network devices providing sensed data) are transformed into meaningful output event streams consumed by subscribers (e.g., a data analytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more user interface windows presented to the user in a display under control of the ESPE independently or through a browser application in an order selectable by the user. For example, a user may execute an ESP application, which causes presentation of a first user interface window, which may include a plurality of menus and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc. associated with the ESP application as understood by a person of skill in the art. As further understood by a person of skill in the art, various operations may be performed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE, thereby instantiating an ESPE at a device, such as machine 220 and/or 240. In an operation 902, the engine container is created. For illustration, ESPE 800 may be instantiated using a function call that specifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 are instantiated by ESPE 800 as a model. The one or more continuous queries 804 may be instantiated with a dedicated thread pool or pools that generate updates as new events stream through ESPE 800. For illustration, the one or more continuous queries 804 may be created to model business processing logic within ESPE 800, to predict events within ESPE 800, to model a physical system within ESPE 800, to predict the physical system state within ESPE 800, etc. For example, as noted, ESPE 800 may be used to support sensor data monitoring and management (e.g., sensing may include force, torque, load, strain, position, temperature, air pressure, fluid flow, chemical properties, resistance, electromagnetic fields, radiation, irradiance, proximity, acoustics, moisture, distance, speed, vibrations, acceleration, electrical potential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.” Instead of storing data and running queries against the stored data, ESPE 800 may store queries and stream data through them to allow continuous analysis of data as it is received. The one or more source windows 806 and the one or more derived windows 808 may be created based on the relational, pattern matching, and procedural algorithms that transform the input event streams into the output event streams to model, simulate, score, test, predict, etc. based on the continuous query model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability is initialized for ESPE 800. In an illustrative embodiment, a pub/sub capability is initialized for each project of the one or more projects 802. To initialize and enable pub/sub capability for ESPE 800, a port number may be provided. Pub/sub clients can use a host name of an ESP device running the ESPE and the port number to establish pub/sub connections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishing device 1022 and event subscribing devices 1024 a-c, according to embodiments of the present technology. ESP system 1000 may include ESP device or subsystem 851, event publishing device 1022, an event subscribing device A 1024 a, an event subscribing device B 1024 b, and an event subscribing device C 1024 c. Input event streams are output to ESP device 851 by publishing device 1022. In alternative embodiments, the input event streams may be created by a plurality of publishing devices. The plurality of publishing devices further may publish event streams to other ESP devices. The one or more continuous queries instantiated by ESPE 800 may analyze and process the input event streams to form output event streams output to event subscribing device A 1024 a, event subscribing device B 1024 b, and event subscribing device C 1024 c. ESP system 1000 may include a greater or a fewer number of event subscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based on indirect addressing. Processed data recipients specify their interest in receiving information from ESPE 800 by subscribing to specific classes of events, while information sources publish events to ESPE 800 without directly addressing the receiving parties. ESPE 800 coordinates the interactions and processes the data. In some cases, the data source receives confirmation that the published information has been received by a data recipient.

A publish/subscribe API may be described as a library that enables an event publisher, such as publishing device 1022, to publish event streams into ESPE 800 or an event subscriber, such as event subscribing device A 1024 a, event subscribing device B 1024 b, and event subscribing device C 1024 c, to subscribe to event streams from ESPE 800. For illustration, one or more publish/subscribe APIs may be defined. Using the publish/subscribe API, an event publishing application may publish event streams into a running event stream processor project source window of ESPE 800, and the event subscription application may subscribe to an event stream processor project source window of ESPE 800.

The publish/subscribe API provides cross-platform connectivity and endianness compatibility between ESP application and other networked applications, such as event publishing applications instantiated at publishing device 1022, and event subscription applications instantiated at one or more of event subscribing device A 1024 a, event subscribing device B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes the publish/subscribe capability of ESPE 800. In an operation 908, the one or more projects 802 are started. The one or more started projects may run in the background on an ESP device. In an operation 910, an event block object is received from one or more computing device of the event publishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, a subscribing client A 1004, a subscribing client B 1006, and a subscribing client C 1008. Publishing client 1002 may be started by an event publishing application executing at publishing device 1022 using the publish/subscribe API. Subscribing client A 1004 may be started by an event subscription application A, executing at event subscribing device A 1024 a using the publish/subscribe API. Subscribing client B 1006 may be started by an event subscription application B executing at event subscribing device B 1024 b using the publish/subscribe API. Subscribing client C 1008 may be started by an event subscription application C executing at event subscribing device C 1024 c using the publish/subscribe API.

An event block object containing one or more event objects is injected into a source window of the one or more source windows 806 from an instance of an event publishing application on event publishing device 1022. The event block object may generated, for example, by the event publishing application and may be received by publishing client 1002. A unique ID may be maintained as the event block object is passed between the one or more source windows 806 and/or the one or more derived windows 808 of ESPE 800, and to subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 and to event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c. Publishing client 1002 may further generate and include a unique embedded transaction ID in the event block object as the event block object is processed by a continuous query, as well as the unique ID that publishing device 1022 assigned to the event block object.

In an operation 912, the event block object is processed through the one or more continuous queries 804. In an operation 914, the processed event block object is output to one or more computing devices of the event subscribing devices 1024 a-c. For example, subscribing client A 1004, subscribing client B 1006, and subscribing client C 1008 may send the received event block object to event subscription device A 1024 a, event subscription device B 1024 b, and event subscription device C 1024 c, respectively.

ESPE 800 maintains the event block containership aspect of the received event blocks from when the event block is published into a source window and works its way through the directed graph defined by the one or more continuous queries 804 with the various event translations before being output to subscribers. Subscribers can correlate a group of subscribed events back to a group of published events by comparing the unique ID of the event block object that a publisher, such as publishing device 1022, attached to the event block object with the event block ID received by the subscriber.

In an operation 916, a determination is made concerning whether or not processing is stopped. If processing is not stopped, processing continues in operation 910 to continue receiving the one or more event streams containing event block objects from the, for example, one or more network devices. If processing is stopped, processing continues in an operation 918. In operation 918, the started projects are stopped. In operation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analytics project after the data is received and stored. In other embodiments, distributed applications process continuously flowing data in real-time from distributed sources by applying queries to the data before distributing the data to geographically distributed recipients. As noted, an event stream processing engine (ESPE) may continuously apply the queries to the data as it is received and determines which entities receive the processed data. This allows for large amounts of data being received and/or collected in a variety of environments to be processed and distributed in real time. For example, as shown with respect to FIG. 2, data may be collected from network devices that may include devices within the internet of things, such as devices within a home automation network. However, such data may be collected from a variety of different resources in a variety of different environments. In any such situation, embodiments of the present technology allow for real-time processing of such data.

Aspects of the current disclosure provide technical solutions to technical problems, such as computing problems that arise when an ESP device fails which results in a complete service interruption and potentially significant data loss. The data loss can be catastrophic when the streamed data is supporting mission critical operations such as those in support of an ongoing manufacturing or drilling operation. An embodiment of an ESP system achieves a rapid and seamless failover of ESPE running at the plurality of ESP devices without service interruption or data loss, thus significantly improving the reliability of an operational system that relies on the live or real-time processing of the data streams. The event publishing systems, the event subscribing systems, and each ESPE not executing at a failed ESP device are not aware of or effected by the failed ESP device. The ESP system may include thousands of event publishing systems and event subscribing systems. The ESP system keeps the failover logic and awareness within the boundaries of out-messaging network connector and out-messaging network device.

In one example embodiment, a system is provided to support a failover when event stream processing (ESP) event blocks. The system includes, but is not limited to, an out-messaging network device and a computing device. The computing device includes, but is not limited to, a processor and a computer-readable medium operably coupled to the processor. The processor is configured to execute an ESP engine (ESPE). The computer-readable medium has instructions stored thereon that, when executed by the processor, cause the computing device to support the failover. An event block object is received from the ESPE that includes a unique identifier. A first status of the computing device as active or standby is determined. When the first status is active, a second status of the computing device as newly active or not newly active is determined. Newly active is determined when the computing device is switched from a standby status to an active status. When the second status is newly active, a last published event block object identifier that uniquely identifies a last published event block object is determined. A next event block object is selected from a non-transitory computer-readable medium accessible by the computing device. The next event block object has an event block object identifier that is greater than the determined last published event block object identifier. The selected next event block object is published to an out-messaging network device. When the second status of the computing device is not newly active, the received event block object is published to the out-messaging network device. When the first status of the computing device is standby, the received event block object is stored in the non-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating and using a machine-learning model according to some aspects. Machine learning is a branch of artificial intelligence that relates to mathematical models that can learn from, categorize, and make predictions about data. Such mathematical models, which can be referred to as machine-learning models, can classify input data among two or more classes; cluster input data among two or more groups; predict a result based on input data; identify patterns or trends in input data; identify a distribution of input data in a space; or any combination of these. Examples of machine-learning models can include (i) neural networks; (ii) decision trees, such as classification trees and regression trees; (iii) classifiers, such as Naïve bias classifiers, logistic regression classifiers, ridge regression classifiers, random forest classifiers, least absolute shrinkage and selector (LASSO) classifiers, and support vector machines; (iv) clusterers, such as k-means clusterers, mean-shift clusterers, and spectral clusterers; (v) factorizers, such as factorization machines, principal component analyzers and kernel principal component analyzers; and (vi) ensembles or other combinations of machine-learning models. In some examples, neural networks can include deep neural networks, feed-forward neural networks, recurrent neural networks, convolutional neural networks, radial basis function (RBF) neural networks, echo state neural networks, long short-term memory neural networks, bi-directional recurrent neural networks, gated neural networks, hierarchical recurrent neural networks, stochastic neural networks, modular neural networks, spiking neural networks, dynamic neural networks, cascading neural networks, neuro-fuzzy neural networks, or any combination of these.

Different machine-learning models may be used interchangeably to perform a task. Examples of tasks that can be performed at least partially using machine-learning models include various types of scoring; bioinformatics; cheminformatics; software engineering; fraud detection; customer segmentation; generating online recommendations; adaptive websites; determining customer lifetime value; search engines; placing advertisements in real time or near real time; classifying DNA sequences; affective computing; performing natural language processing and understanding; object recognition and computer vision; robotic locomotion; playing games; optimization and metaheuristics; detecting network intrusions; medical diagnosis and monitoring; or predicting when an asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to create machine-learning models. Examples of tools for creating and managing machine-learning models can include SAS® Enterprise Miner, SAS® Rapid Predictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services (CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partially automated (e.g., with little or no human involvement) process called training. During training, input data can be iteratively supplied to a machine-learning model to enable the machine-learning model to identify patterns related to the input data or to identify relationships between the input data and output data. With training, the machine-learning model can be transformed from an untrained state to a trained state. Input data can be split into one or more training sets and one or more validation sets, and the training process may be repeated multiple times. The splitting may follow a k-fold cross-validation rule, a leave-one-out-rule, a leave-p-out rule, or a holdout rule. An overview of training and using a machine-learning model is described below with respect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the training data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The training data can be used in its raw form for training a machine-learning model or pre-processed into another form, which can then be used for training the machine-learning model. For example, the raw form of the training data can be smoothed, truncated, aggregated, clustered, or otherwise manipulated into another form, which can then be used for training the machine-learning model.

In block 1106, a machine-learning model is trained using the training data. The machine-learning model can be trained in a supervised, unsupervised, or semi-supervised manner. In supervised training, each input in the training data is correlated to a desired output. This desired output may be a scalar, a vector, or a different type of data structure such as text or an image. This may enable the machine-learning model to learn a mapping between the inputs and desired outputs. In unsupervised training, the training data includes inputs, but not desired outputs, so that the machine-learning model has to find structure in the inputs on its own. In semi-supervised training, only some of the inputs in the training data are correlated to desired outputs.

In block 1108, the machine-learning model is evaluated. For example, an evaluation dataset can be obtained, for example, via user input or from a database. The evaluation dataset can include inputs correlated to desired outputs. The inputs can be provided to the machine-learning model and the outputs from the machine-learning model can be compared to the desired outputs. If the outputs from the machine-learning model closely correspond with the desired outputs, the machine-learning model may have a high degree of accuracy. For example, if 90% or more of the outputs from the machine-learning model are the same as the desired outputs in the evaluation dataset, the machine-learning model may have a high degree of accuracy. Otherwise, the machine-learning model may have a low degree of accuracy. The 90% number is an example only. A realistic and desirable accuracy percentage is dependent on the problem and the data.

In some examples, if the machine-learning model has an inadequate degree of accuracy for a particular task, the process can return to block 1106, where the machine-learning model can be further trained using additional training data or otherwise modified to improve accuracy. If the machine-learning model has an adequate degree of accuracy for the particular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data is received from a remote database or a local database, constructed from various subsets of data, or input by a user. The new data may be unknown to the machine-learning model. For example, the machine-learning model may not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze the new data and provide a result. For example, the new data can be provided as input to the trained machine-learning model. The trained machine-learning model can analyze the new data and provide a result that includes a classification of the new data into a particular class, a clustering of the new data into a particular group, a prediction based on the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result can be added to, multiplied with, or otherwise combined with other data as part of a job. As another example, the result can be transformed from a first format, such as a time series format, into another format, such as a count series format. Any number and combination of operations can be performed on the result during post-processing.

A more specific example of a machine-learning model is the neural network 1200 shown in FIG. 12. The neural network 1200 is represented as multiple layers of interconnected neurons, such as neuron 1208, that can exchange data between one another. The layers include an input layer 1202 for receiving input data, a hidden layer 1204, and an output layer 1206 for providing a result. The hidden layer 1204 is referred to as hidden because it may not be directly observable or have its input directly accessible during the normal functioning of the neural network 1200. Although the neural network 1200 is shown as having a specific number of layers and neurons for exemplary purposes, the neural network 1200 can have any number and combination of layers, and each layer can have any number and combination of neurons.

The neurons and connections between the neurons can have numeric weights, which can be tuned during training. For example, training data can be provided to the input layer 1202 of the neural network 1200, and the neural network 1200 can use the training data to tune one or more numeric weights of the neural network 1200. In some examples, the neural network 1200 can be trained using backpropagation. Backpropagation can include determining a gradient of a particular numeric weight based on a difference between an actual output of the neural network 1200 and a desired output of the neural network 1200. Based on the gradient, one or more numeric weights of the neural network 1200 can be updated to reduce the difference, thereby increasing the accuracy of the neural network 1200. This process can be repeated multiple times to train the neural network 1200. For example, this process can be repeated hundreds or thousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neural network. In a feed-forward neural network, every neuron only propagates an output value to a subsequent layer of the neural network 1200. For example, data may only move one direction (forward) from one neuron to the next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neural network. A recurrent neural network can include one or more feedback loops, allowing data to propagate in both forward and backward through the neural network 1200. This can allow for information to persist within the recurrent neural network. For example, a recurrent neural network can determine an output based at least partially on information that the recurrent neural network has seen before, giving the recurrent neural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vector of numbers from one layer; transforming the vector of numbers into a new vector of numbers using a matrix of numeric weights, a nonlinearity, or both; and providing the new vector of numbers to a subsequent layer of the neural network 1200. Each subsequent layer of the neural network 1200 can repeat this process until the neural network 1200 outputs a final result at the output layer 1206. For example, the neural network 1200 can receive a vector of numbers as an input at the input layer 1202. The neural network 1200 can multiply the vector of numbers by a matrix of numeric weights to determine a weighted vector. The matrix of numeric weights can be tuned during the training of the neural network 1200. The neural network 1200 can transform the weighted vector using a nonlinearity, such as a sigmoid tangent or the hyperbolic tangent. In some examples, the nonlinearity can include a rectified linear unit, which can be expressed using the equation y=max(x, 0) where y is the output and x is an input value from the weighted vector. The transformed output can be supplied to a subsequent layer, such as the hidden layer 1204, of the neural network 1200. The subsequent layer of the neural network 1200 can receive the transformed output, multiply the transformed output by a matrix of numeric weights and a nonlinearity, and provide the result to yet another layer of the neural network 1200. This process continues until the neural network 1200 outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number and combination of machine-learning models having any number and combination of characteristics. The machine-learning model(s) can be trained in a supervised, semi-supervised, or unsupervised manner, or any combination of these. The machine-learning model(s) can be implemented using a single computing device or multiple computing devices, such as the communications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part by using machine-learning models can reduce the total number of processing iterations, time, memory, electrical power, or any combination of these consumed by a computing device when analyzing data. For example, a neural network may more readily identify patterns in data than other approaches. This may enable the neural network to analyze the data using fewer processing cycles and less memory than other approaches, while obtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedily executed and processed with machine-learning specific processors (e.g., not a generic CPU). Such processors may also provide an energy savings when compared to generic CPUs. For example, some of these processors can include a graphical processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an artificial intelligence (AI) accelerator, a neural computing core, a neural computing engine, a neural processing unit, a purpose-built chip architecture for deep learning, and/or some other machine-learning specific processor that implements a machine learning approach or one or more neural networks using semiconductor (e.g., silicon (Si), gallium arsenide (GaAs)) devices. These processors may also be employed in heterogeneous computing architectures with a number of and a variety of different types of cores, engines, nodes, and/or layers to achieve various energy efficiencies, processing speed improvements, data communication speed improvements, and/or data efficiency targets and improvements throughout various parts of the system when compared to a homogeneous computing architecture that employs CPUs for general purpose computing.

FIGS. 13A and 13B illustrate two different example embodiments of a processing system 2000, and FIG. 14 illustrates an example of the use of either of these embodiments of the processing system 2000 to perform the pre-processing operations and subsequent speech-to-text processing operations. More specifically, FIG. 13A illustrates a block diagram of an example embodiment of a distributed processing system 2000 incorporating one or more storage devices 2100 that may form a storage grid 2001, one or more node devices 2300 that may form of a node device grid 2003, at least one control device 2500 and/or at least one requesting device 2700 coupled by a network 2999. FIG. 13B illustrates a block diagram of an alternate example embodiment of a non-distributed processing system 2000 in which the processing functionality of the one or more node devices 2300 is incorporated into the at least one control device 2500.

Turning to FIG. 13A, the storage device(s) 2100 may store one or more speech data sets 3100 in which speech audio may be stored in any of a variety of digital audio storage formats. Where there are multiple storage devices 2100, at least a subset of the one or more speech data sets 3100 may be stored in a distributed manner in which different portions thereof are stored within different ones of the storage devices 2100. Each of the one or more speech data sets 3100 may be so stored within or retrieved from the storage device(s) 2100 by the one or more node devices 2300 under the control of the control device 2500. More specifically, in support of a distributed performance of at least some of the pre-processing operations and/or speech-to-text processing operations across multiple node devices 2300, a speech data set 3100 may be divided into data chunks 3110 that each represent a chunk of the speech audio of the speech data set 3100, and/or may be divided into data segments 3140 that each represent a speech segment of that speech audio. Those data chunks 3110 and/or data segments 3140 may then be distributed among, and/or otherwise provided to, multiple ones of the node devices 2300 from different ones of the storage devices 2100.

The storage device(s) 2100 may also store one or more corpus data sets 3400 that each represent a language model implemented as a corpus of a particular language, and/or one or more text data sets 3700 that each represent a transcript of speech audio that may be stored as a speech data set 3100. As with the one or more speech data sets 3100, where there are multiple storage devices 2100, at least a subset of the one or more corpus data sets 3400, and/or at least a subset of the one or more text data sets 3700, may be stored in a distributed manner in which different portions thereof are stored within different ones of the storage devices 2100. In support of distributed speech-to-text processing operations, multiple copies of the entirety of a corpus data set 3400 may be provided to each of multiple ones of the node devices 2300.

In support of such operations, the devices 2100, 2300, 2500 and/or 2700 may exchange such portions of a speech data set 3100, may exchange copies of a corpus data set 3400, and/or may exchange other information concerning speech audio pre-processing operations and/or speech-to-text processing operations through the network 2999. In various embodiments, the network 2999 may be a single network that may extend within a single building or other relatively limited area, a combination of connected networks that may extend a considerable distance, and/or may include the Internet. Thus, the network 2999 may be based on any of a variety (or combination) of communications technologies by which communications may be effected, including without limitation, wired technologies employing electrically and/or optically conductive cabling, and wireless technologies employing infrared, radio frequency (RF) or other forms of wireless transmission.

Each of the speech data sets 3100 may be any of a variety of types of digital data representation of any of a variety of types of speech audio. Such representations of speech audio may include a series of amplitude values of one or more audio channels of any of a variety of bit widths (e.g., 8-bit, 12-bit, 16-bit, 20-bit or 24-bit), captured at any of a variety of sampling rates (e.g., 41.1 kHz, 48 kHz, 88.2 kHz or 96 kHz), and stored in any of a variety of widely used compressed or uncompressed audio data formats (e.g., MP3 (Motion Picture Experts Group layer 3), WAV (Waveform Audio), PCM (Pulse-Code Modulation), FLAC (Free Lossless Audio Codec)), Dolby Digital or TrueHD of Dolby Laboratories of San Francisco, Calif., USA, or THX Ultra2 or Select2 of THX Ltd. of San Francisco, Calif., USA). In some embodiments, the speech data set 3100 may include other data beyond speech audio, such as corresponding video, corresponding still images (e.g., a corresponding slide show of still images), alternate corresponding speech audio in a different language, etc. In some of such embodiments, the speech data set 3100 may any of a variety of types of “container” format or other data format that supports the provision of a multimedia or other combined audio and video presentation (e.g., MP4 of the International Organization for Standardization of Geneva, Switzerland).

The speech audio that is so represented may include any of a variety of types of speech, including and not limited to, telephone and/or radio conversations (e.g., telephone service calls, or air traffic control communications), telephone messages or other forms of voice mail, audio from in-person and/or remote conferences, lecture speech, audio tracks from entertainment programs that include speech audio (e.g., audio from movies or from musical performances), verbal narrations of stories and/or of events in progress (e.g., narrations of sports events or other news events), and/or verbal commands to local electronic devices and/or to servers providing online services, etc.

At least a subset of the speech data sets 3100 stored by the one or more storage devices 2100 may each represent a stored recording of speech audio that was fully captured at an earlier time. Thus, such speech data set(s) 3100 may represent speech audio that may have been recorded either relatively recently (e.g., within recent minutes or hours), or long ago (e.g., weeks, months or years earlier). Alternatively or additionally, at least another subset of the speech data sets 3100 may each represent just a stored portion of speech audio that is still in the process of being captured. Thus, such speech data set(s) 3100 may serve, at least temporarily, as buffer(s) of portions of ongoing speech audio that have already been captured, with more portions thereof still in the process of being captured.

It is envisioned that at least a subset of the speech data sets 3100 may be sufficiently large in size such that storage and/or processing of the entirety thereof within a single device may be deemed to be at least impractical, if not impossible. Therefore, to facilitate storage and/or processing of such larger speech data sets 3100 in a distributed manner across multiple devices, each of such larger speech data sets 3100 may be divided into multiple portions that may be distributed among multiple storage devices 2100 and/or among multiple node devices 2300.

In some embodiments, multiple ones of the storage devices 2100 may be operated together (e.g., as a network-attached drive array, etc.) primarily for the purpose of persistently storing data, such as the one or more speech data sets 3100. In such embodiments, the multiple storage devices 2100 may be capable of exchanging the entirety of a relatively large speech data set 3100 with multiple node devices 2300 in a set of data transfers of portions thereof (e.g., data chunks 3110 thereof, or data segments 3140 thereof) performed at least partially in parallel through the network 2999, and such transfers may be coordinated by the control device 2500. In some embodiments, processor(s) of the one or more storage devices 2100 may each independently implement a local file system by which at least relatively small speech data sets 3100 may each be stored entirely within a single one of the storage devices 2100. Alternatively or additionally, multiple ones of the storage devices 2100 may cooperate through the network 2999 to implement a distributed file system to store larger speech data sets 3100 as multiple portions in a distributed manner across multiple ones of the storage devices 2100. As still another alternative, it may be that one or more of the storage devices 2100 store a combination of whole speech data sets 3100 that are of relatively small data size such that they are able to be stored entirely within a single storage device 2100, and a portion of at least one speech data set 3100 that is too large in data size to be able to be stored entirely within any single one of the storage devices 2100.

In various embodiments, each of the multiple node devices 2300 may incorporate one or more processors 2350, one or more neural networks 2355, a storage 2360, and/or a network interface 2390 to couple each of the node devices 2300 to the network 2999. The processor(s) 2350 may incorporate multiple processing cores 2351 and/or other features to support the execution of multiple executable routines and/or multiple instances of executable routine(s) across multiple execution threads. The storage 2360 may store control routines 2310, 2340 and/or 2370; one or more data chunks 3110; one or more data segments 3140; and/or a corpus data set 3400.

Each of the control routines 2310, 2340 and 2370 may incorporate a sequence of instructions operative on the processor(s) 2350 to implement logic to perform various functions. Referring briefly to FIG. 14 in addition to FIG. 13A, in executing the control routine 2310, the processor(s) 2350 of each of the node devices 2300 may be caused to perform various pre-processing operations, such as normalization of the digital audio storage format in which the chunk of speech audio within each data chunk 3110 is stored, and/or determining the manner in which a speech data set 3100 is to be divided into data segments 3140 thereof as input to speech-to-text processing operations. In executing the control routine 2340, the processor(s) 2350 of each of the node devices 2300 may be caused to perform various speech-to-text processing operations, such as feature detection to identify acoustic features within the speech segment of each data segment 3140, use multiple instances of an acoustic model to identify likely graphemes, and/or use multiple instances of an n-gram language model (stored as a corpus data set 3400) to assist in identifying likely words to generate a transcript of the speech audio of the speech data set 3100, which may then be stored within the one or more storage devices 2100 as a corresponding text data set 3700. In executing the control routine 2370, the processor(s) 2350 of each of the node devices 2300 may be caused to perform various post-processing operations, such as text analytics to derive various insights concerning the contents of speech audio stored as a speech data set 3100, and/or the generation of various visualizations for presenting such insights.

Returning to FIG. 13A, as will be explained in greater detail, in performing at least a subset of pre-processing operations, at least a subset of processing operations and/or at least a subset of post-processing operations, the processor(s) 2350 of multiple ones of the node devices 2300 may be caused to perform such operations at least partially in parallel. As has been explained, this may be at least partially due to the size of speech data set 3100. Alternatively or additionally, this may be at least partially due to a need or desire to increase the speed and/or efficiency with which such operations are performed, regardless of the size of a speech data set 3100. Regardless of the motivation, such at least partially parallel performance of pre-processing and/or processing operations may be coordinated by the control device 2500 through the network 2999.

In various embodiments, the control device 2500 may incorporate one or more processors 2550, one or more neural networks 2555, a storage 2560, and/or a network interface 2590 to couple the control device 2500 to the network 2999. The processor(s) 2550 may incorporate multiple processing cores 2551 and/or other features to support the execution of multiple executable routines and/or multiple instances of executable routine(s) across multiple execution threads. The storage 2560 may store control routines 2510, 2540 and/or 2570, configuration data 2335, one or more data chunks 3110, one or more data segments 3140, and/or a text data set 3700.

Each of the control routines 2510, 2540 and 2570 may incorporate a sequence of instructions operative on the processor(s) 2550 to implement logic to perform various functions. Again, referring briefly to FIG. 14 in addition to FIG. 13A, in executing the control routine 2510, the processor(s) 2550 of the control device 2500 may be caused to operate the network interface 2590 to coordinate, via the network 2999, at least a subset of the pre-processing operations performed, at least partially in parallel, by processors 2350 of multiple ones of the node devices 2300 as a result of executing corresponding instances of the control routine 2310. More specifically, the processors 2550 may be caused to coordinate the performances of multiple segmentation techniques across multiple ones of the node devices 2300. Alternatively or additionally, as candidate sets of likely sentence pauses are derived from the performance of each segmentation technique, it may be that processor(s) 2550 of the control device 2500 are caused by the control routine 2510 to use the candidate sets received from multiple node devices 2300 to derive a converged set 3119 of likely sentence pauses.

In executing the control routine 2540, the processor(s) 2550 of the control device 2500 may be caused to operate the network interface 2590 to coordinate, via the network 2999, at least a subset of the speech-to-text processing operations performed, at least partially in parallel, by processors 2350 of multiple ones of the node device 2300 as a result of executing corresponding instances of the control routine 2340. More specifically, the processors 2550 may be caused to coordinate the generation of data segments 3140 among the node devices 2300 based on the indications of likely sentence pauses within the converged set 3119 of likely sentence pauses that were derived earlier during pre-processing. Alternatively or additionally, the processors 2550 may be caused to coordinate the detection of acoustic features within the speech segment of each of the data segments 3140, and/or to coordinate the use of multiple instances of an acoustic model to identify likely text characters across multiple ones of the node devices 2300. Alternatively or additionally, as sets of probability distributions of likely graphemes are derived from such use of acoustic models, it may be that the processor(s) 2550 of the control device 2500 are caused by the control routine 2540 to use the sets of probability distributions received from multiple node devices 2300 as inputs to coordinate beam searches of multiple instances of an n-gram language model across multiple node devices 2300 to generate the transcript of the speech audio of the speech data set 3100.

In executing the control routine 2570, the processor(s) 2550 of the control device 2500 may be caused to operate the network interface 2590 to coordinate, via the network 2999, various post-processing operations performed, at least partially in parallel, by processors 2350 of multiple ones of the node device 2300 as a result of executing corresponding instances of the control routine 2340. More specifically, the processors 2550 may be caused to coordinate the distributed use of various forms of text analytics among the node devices 2300 to derive insights concerning the speech audio of the speech data set 3100.

Returning to FIG. 13A, in various embodiments, the requesting device 2700 may incorporate one or more of a processor 2750, a storage 2760, an input device 2720, a display 2780, and a network interface 2790 to couple the requesting device 2700 to the network 2999. The storage 2760 may store a control routine 2740, and/or a text data set 3700.

The control routine 2740 may incorporate a sequence of instructions operative on the processor 2750 to implement logic to perform various functions. In executing the control routine 2740, the processor 2750 of the requesting device 2700 may be caused to operate the input device 2720 and/or the display 2780 to provide a user interface (UI) by which an operator of the requesting device 2700 may transmit a request to the control device 2500 to perform one or more operations that may include speech-to-text conversion of the speech audio represented by a specified one of the speech data sets 3100. The processor 2750 may be subsequently caused to similarly provide a UI by which the operator of the requesting device 2700 is able to view the text of that speech audio upon receipt of its transcript in the form of a text data set 3700 from the control device 2500, and/or is able to view various derived insights concerning the transcript.

Comparing FIGS. 13A and 13B, as an alternative to the distributed processing system 2000 of FIG. 13A including multiple node device(s) 2300 among which at least a subset of pre-processing, speech-to-text processing and/or post-operations may be performed at least partially in parallel in a distributed manner, it may instead be the case that such at least partially parallel performances are to be distributed across multiple processor cores 2551 of the processor(s) 2550 of the control device 2500, as depicted in the processing system 2000 of FIG. 13B. As also depicted in FIG. 13B, it may be that the processing system 2000 does include the one or more storage devices 2100 of FIG. 13A, and that it is the control device 2500 that exchanges portions of data speech sets 3100 directly with storage device(s) 2100 in lieu of their being separate and distinct node devices 2300 to do so. Alternatively or additionally (and not specifically depicted), it may be that the processing system 2000 of FIG. 13B does not include the one or more storage devices 2100 of FIG. 13A, and that the control device 2500 directly stores one or more speech data sets 3100, one or more corpus data sets 3400, and/or one or more text data sets 3700.

FIGS. 15A, 15B and 15C, taken together, illustrate an example of use of an adaptive peak amplitude (APA) segmentation technique during pre-processing to enable the division of the speech audio of a speech data set 3100 into segments (each represented in storage by a data segment 3140), where the divisions into segments occur at the midpoints of sentence pauses. FIG. 15A illustrates the initial division of the speech data set 3100 into data chunks 3110 a that each represent a chunk of the speech audio of the speech data set 3100, and the measurement of peak amplitude levels to derive a threshold amplitude 2232. FIG. 15B illustrates the categorization of each of the chunks as either a speech chunk or a pause chunk. FIG. 15C illustrates the identification of a candidate set 3118 a of likely sentence pauses within the speech audio of the speech data set 3100.

As previously discussed, in the distributed processing system 2000 depicted in FIG. 13A (or in another similar distributed processing system), it may be that each of the multiple segmentation techniques is assigned to be performed by a different one of the node devices 2300. Thus, each one of such assigned node devices 2300 derives a different candidate set 3118 of likely sentence pauses for subsequent use within the control device 2500 to derive a converged set 3119 of likely sentence pauses to be used as the basis for dividing the speech audio of the speech data set 3100. However, as also previously discussed, in the non-distributed processing system 2000 depicted in FIG. 13B (or in another similar processing system), it may be that each of the multiple segmentation techniques is assigned to be performed within a separate one of multiple execution threads supported by multiple cores 2551 of the processor(s) 2550 within the control device 2500. Thus, each of the multiple candidate sets 3118 of likely sentence pauses would be derived on a different one of those assigned execution threads within the control device 2500, before being used to derive the converged set 3119 on what may be yet another execution thread within the control device 2500.

Turning to FIG. 15A, in executing a division component 2311 of the control routine 2310, either core(s) 2351 of a processor 2350 of a node device 2300 a, or core(s) 2551 of a processor 2550 of the control device 2500 may be caused to divide a speech data set 3100 into multiple data chunks 3110 a. In so doing, an indication of the length of the speech audio that is to be represented by each data chunk 3110 a may be retrieved from the configuration data 2335.

It should be noted that, in some embodiments, the pre-processing of speech audio as part of speech-to-text conversion may also include normalizing the digital format in which the speech audio is stored as a speech data set 3100. Thus, it may be, that prior to or as part of dividing the speech audio into chunks, the digital format in which the speech audio is stored as the speech data set 3100 may be changed to a pre-selected format that specifies one or more of a particular sampling frequency, data width and/or type of data value per sample, a particular type of compression (or no compression), etc. It may be that such a pre-selected format is necessitated for sake of compatibility with one or more components for performing one or more of the pre-processing operations, and/or one or more of the processing operations of the speech-to-text conversion.

In executing an amplitude component 2312 of the control routine 2310, core(s) of the processor 2350 or 2550 may be caused to analyze each of the data chunks 3110 a to measure the peak amplitude of the chunk of speech audio present within each. With all of the peak amplitudes across all of the data chunks 3110 a so measured, a level of amplitude of a preselected percentile of all of the peak amplitudes may be derived and used as a threshold amplitude 2232. In so doing, an indication of the preselected percentile may be retrieved from the configuration data 2335.

As previously discussed, it may be that the multiple segmentation techniques are assigned relative weighting factors that are used in combining the resulting multiple candidate sets 3118 of likely sentence pauses to derive the converged set 3119 of likely sentence pauses, and it may be that the relative weighting factors are adjusted based on the level of audio noise that is present across the chunks of the speech audio. In such embodiments, and as depicted, it may be that execution of the amplitude component 2312 also causes the measurement of the level of audio noise in the chunk of speech audio within each of the data chunks 3110 a, and causes the derivation of an audio noise level 2235 that is in some way representative of the level of audio noise present within the entire speech audio. In various embodiments, the audio noise level 2235 may be indicative of the minimum level of audio noise measured across all of the data chunks 3110 a, an average thereof, and/or of any of a variety of other characteristics of audio noise.

Turning to FIG. 15B, in executing a categorization component 2313 of the control routine 2310, core(s) of the processor 2350 or 2550 may be caused to use the threshold amplitude 2232 to categorize each of the data chunks 3110 a as either a speech data chunk 3110 s or a pause data chunk 3110 p. More specifically, all of the data chunks 3110 a that each represent a chunk of speech audio with a measured peak amplitude above the threshold amplitude are deemed to be speech data chunks 3110 s that each represent a speech chunk, while all of the data chunks 3110 a that each represent a chunk of speech audio with a measured peak amplitude below the threshold amplitude are deemed to be pause data chunks 3110 p that each represent a pause chunk. Data chunks 3110 a that each represent a chunk of speech audio with a measured peak amplitude equal to the threshold amplitude may be deemed to be speech data chunks 3110 s or pause data chunks 3110 p, depending on implementation details in various embodiments.

Turning to FIG. 15C, in executing a pause identification component 2317 of the control routine 2310, core(s) of the processor 2350 or 2550 may be caused to adaptively identify longer pauses defined by larger quantities of consecutive pause data chunks 3110 p as likely sentence pauses. More specifically, and starting with the data chunk 3110 a that represents the temporally earliest chunk of the speech audio of the speech data set 3100, a window 2236 that covers a preselected quantity of temporally consecutive ones of the data chunks 3110 a may be shifted across the length of the speech audio, starting with the temporally earliest data chunk 3110 a and proceeding throughout all of the data chunks 3110 a in temporal order toward the temporally last data chunk 3110 a. Thus, with the window 2236 positioned to begin with the earliest data chunk 3110 a (regardless of whether it is a pause data chunk 3110 p or a speech data chunk 3110 s), measurements of the lengths of each pause represented by multiple consecutive pause data chunks 3110 p within the window 2236 (if there are any pauses represented by multiple consecutive pause data chunks 3110 p within the window 2236) may be taken to identify the longest pause thereamong. The longest pause that is so identified within the window 2236 (i.e., the pause represented by the greatest quantity of consecutive pause chunks 3110 p) may then be deemed likely to be a sentence pause.

The window 2236 may then be shifted away from the earliest data chunk 3110 a and along the data chunks 3110 of the speech audio in temporal order so as to cause the window 2236 to next begin either amidst the just-identified likely sentence pause (e.g., beginning at the midpoint thereof) of just after the just-identified likely sentence pause (e.g., as depicted, immediately after the temporally last chunk of the consecutive pause chunks 3110 p that define the just-identified likely sentence pause). With the window 2236 so repositioned, again, measurements of the lengths of each pause represented by multiple consecutive pause data chunks 3110 p within the window 2236 may be taken to again identify the longest pause thereamong. Again, the longest pause that is so identified within the window (i.e., the pause represented by the greatest quantity of consecutive pause chunks 3110 p) may be deemed likely to be a sentence pause. As depicted, this may be repeated until the window 2236 has been shifted along the entirety of the length of the speech audio (i.e., from the temporally earliest data chunk 3110 a to the temporally latest data chunk 3110 a).

Each of the pauses that has been deemed a likely sentence pause within the speech audio 3100 may form part of the candidate set 3118 a of likely sentence pauses derived using the APA segmentation technique. More precisely, indications of where each likely sentence pause starts and ends within the speech audio may be stored within the candidate set 3118 a, and/or indications of where the midpoint of each likely sentence pause is located within the speech audio and/or its length may be so stored. The manner in which such locations within the speech audio are described may be as amounts of time from the beginning of the speech audio represented by the speech data set 3100.

In so identifying likely sentence pauses through such use of the window 2236, it may be that an indication of what the length of the window 2236 should be (i.e., how many consecutive data chunks 3110 a it should span) may be retrieved from the configuration data 2335. The length of the window 2236 may be selected to ensure that there cannot be a distance between the midpoints of any adjacent pair of likely sentence pauses that is greater than a capacity limitation that may be present in subsequent processing operations of the speech-to-text conversion. Alternatively or additionally, the length of the window 2236 may be selected to increase the likelihood that a sentence pause will be identified each time the window 2236 is re-positioned, based on the typical length of sentences in whichever language is used for the speech audio.

Further, in some embodiments, it may be that any instances of an adjacent pair of likely sentence pauses that are closer to each other than a predetermined threshold period of time are not permitted. An indication of the length of the predetermined threshold period of time (which may also be expressed as a quantity of consecutive data chunks 3110 a) may also be retrieved from the configuration data 2335. It may be that, wherever such a pair of likely sentence pauses might occur, one of the two likely sentence pauses may be dropped from those that are included in the candidate set 3118 a of likely sentence pauses. The selection of which of two such likely sentence pauses is the one to be dropped may be based on which is shorter than the other, and/or may be based on a requirement that the dropping of one or the other should not be allowed to create a distance between any of two of the remaining likely sentence pauses that is greater than the length of the window 2236, which may be treated as an upper limit on the distance between any two of the likely sentence pauses.

FIGS. 16A and 16B, taken together, illustrate an example of use of a connectionist temporal classification (CTC) segmentation technique during pre-processing to also enable the division of the same speech data set 3100 into segments. FIG. 16A illustrates the initial division of the speech data set 3100 into data chunks 3110 c that each represent a chunk of the speech audio of the speech data set 3100, and the provision of those data chunks 3110 c as an input to a neural network 2355 of one of the node devices 2300, or as an input to a neural network 2555 of the control device 2500. FIG. 16B illustrates the use of such a neural network, which has been configured to implement an acoustic model, to identify likely sentence pauses for inclusion in a candidate set 3118 c of likely sentence pauses within the speech audio.

Again, in the distributed processing system 2000 depicted in FIG. 13A (or in another similar distributed processing system), it may be that each of the multiple segmentation techniques is assigned to be performed by a different one of the node devices 2300. However, again, in the non-distributed processing system 2000 depicted in FIG. 13B (or in another similar processing system), it may be that each of the multiple segmentation techniques is assigned to be performed within a separate one of multiple execution threads supported by multiple cores 2551 of the processor(s) 2550 within the control device 2500. Therefore, and by way of example, it may be that the APA segmentation technique described in detail above in reference to FIGS. 15A-C may be performed within the node device 2300 a to derive the candidate set 3118 a of likely sentence pauses, while the CTC segmentation technique that is about to described in reference to FIGS. 16A-B may be performed, at least partially in parallel, within another node device 2300 c to derive the corresponding candidate set 3118 c of likely sentence pauses. Then, at least these two candidate sets 3118 a and 3118 c of likely sentence pauses may be combined within the control device 2500 to generate the converged set 3119 on an execution thread within the control device 2500.

Turning to FIG. 16A, in executing the division component 2311 of the control routine 2310, either core(s) 2351 of a processor 2350 of a node device 2300 c, or core(s) 2551 of a processor 2550 of the control device 2500 may be caused to divide the same speech data set 3100 as was featured in FIGS. 15A-C into multiple data chunks 3110 c. In so doing, an indication of the length of the speech audio that is to be represented by each data chunk 3110 c may be retrieved from the configuration data 2335. It should be noted that the data chunks 3110 c of this CTC segmentation technique may not represent the same length of the speech audio as are represented by the data chunks 3110 a of the APA segmentation technique of FIGS. 15A-C. Indeed, it is envisioned that the data chunks 3110 c are each likely to represent a greater length of speech audio such that the speech audio represented by a single one of the data chunks 3110 c may match the length of the speech audio represented by multiple ones of the data chunks 3110 a.

Again, in some embodiments, the pre-processing of speech audio as part of speech-to-text conversion may include normalizing the digital format in which the speech audio is stored as a speech data set 3100. Thus, it may again be that, prior to or as part of dividing the speech audio into chunks, the digital format in which the speech audio is stored may be changed to a pre-selected format that specifies one or more of a particular sampling frequency, data width and/or type of data value per sample, a particular type of compression (or no compression), etc.

As will be familiar to those skilled in the art, at least some acoustic models implemented using neural networks (and/or other technologies) may accept indications of detected audio features as input, instead of accepting audio data (e.g., the data chunks 3110 c) directly as input. To accommodate the use of such implementations of an acoustic model, execution of the control routine 2310 may entail execution of a feature detection component 2312 to analyze the portion of speech audio represented by each data chunk 3110 c to identify instances of each of a pre-selected set of acoustic features. In so doing, either core(s) 2351 of a processor 2350 of a node device 2300 c, or core(s) 2551 of a processor 2550 of the control device 2500 may be caused to generate a corresponding feature vector 3112 from each data chunk 3110 c that is analyzed. Each feature vector 3112 may include indications of each acoustic feature that is identified and when it occurred within the speech audio of the corresponding data chunk 3110 c.

In executing a configuration component 2315, core(s) 2351 of the processor 2350 of the node device 2300 c may be caused to configure a neural network 2355 therein to implement an acoustic model, or core(s) 2551 of the processor 2550 of the control device 2500 may be caused to so configure a neural network 2555 therein. As previously discussed, and as depicted, the neural network 2355 or 2555 incorporates a CTC output 2356 or 2556, respectively, thereby augmenting the output of text characters with the output of blank symbols.

As previously discussed, a neural network incorporating a CTC output, and that has been trained to implement an acoustic model, is normally used to accept indications of acoustic features detected within speech audio, and to output indications of the probabilities of which one or more text characters are likely to correspond to those acoustic features (e.g., probability distributions for text characters). With the addition of the CTC output, the probabilistic indications of likely text characters are augmented with blank symbols that are intended to identify instances where there are likely to be consecutive occurrences of the same text character (e.g., the pair of “1” characters in the word “bell”), despite the absence of an acoustic feature that would specifically indicate such a situation (e.g., no acoustic feature in the pronunciation of the “1” sound in the word “bell” that indicates that there are two consecutive “1” characters therein).

Broadly, CTC outputs have been used to aid in temporally aligning a sequence of indications of features that have been observed (e.g., acoustic features in speech sounds, or visual features in handwriting), with a sequence of labels (e.g., text characters, phonemes and/or graphemes) where there may be differences between the density of input observations over a period of time and the density of labels that are output for that same period of time. Such a CTC output has been used to generate blank symbols that may be used as a guide in performing such an alignment, including blank symbols that indicate where there may be multiple ones of the same label that are consecutively output that might otherwise be mistakenly merged into a single instance of that label (as in the above-described situation of a pair of “1” text characters that should not be merged). In this way, such multiple consecutive instances of a label (e.g., of a text character) are able to be associated with what may be a single observation, or a single set of observations, that might otherwise be associated with only one instance of that label, thereby aiding in the proper aligning of the input and output sequences.

However, it has been observed (and then confirmed by experimentation) that such a trained neural network with a CTC output may also be useful in identifying sentence pauses. More specifically, it has been observed that, in addition to outputting single blank symbols for such consecutive instances of a text character, the CTC output also has a tendency to generate relatively long strings of consecutive blank symbols that correspond quite well to where sentence pauses occur.

Turning to FIG. 16B, in so using the neural network 2355 or 2555 for the detection of sentence pauses, each data chunk 3110 c is provided to the neural network 2355 or 2555 as an input. In executing the pause identification component 2317, core(s) of the processor 2350 or 2550 are caused to monitor the corresponding CTC output for occurrences of strings of consecutive blank symbols. FIG. 16B depicts an example of three consecutive data chunks 3110 c that each represent a different depicted portion of speech audio in which the words “Hello” and “Please leave a message” are spoken as two separate sentences.

Turning to the provision of the first of the three data chunks 3110 c that represents the speech sounds for portions of the words “Hello” and “Please” as an input, the output includes the letters therefor, accompanied by instances of the blank symbol (indicated in FIG. 16B using the “{circumflex over ( )}” character) separating the corresponding characters. As shown, a single instance of the blank symbol may be output between the two consecutive instances of the “1” character from the word “Hello”, thereby exemplifying the aforedescribed function for which the CTC output is typically relied upon to perform. However, as also shown, an instance of a relatively long string of consecutive blank symbols is also output that corresponds with the sentence pause that occurs between these two words.

Turning to the provision of the second of the three data chunks 3110 c that represents the speech sounds for another portion of the word “Please” and the entirety of each of the two words “leave” and “a” as input, the output includes the letters therefor, also accompanied by instances of the blank symbol separating the corresponding characters. As shown, two instances of a relatively short string of consecutive blank symbols are also output that each correspond with one of the two pauses that occur between adjacent pairs of these three words.

Turning to the provision of the third of the three data chunks 3110 c that represents the speech sounds for just the word “message” as input, the output includes the letters therefor, also accompanied by instances of the blank symbol separating the corresponding characters. As shown, a single instance of the blank symbol may be output between the two consecutive instances of the “s” character from this word, thereby again exemplifying the aforedescribed function for which the CTC output is typically relied upon to perform.

As each of these outputs are provided by the neural network 2355 or 2555, the length of each string of consecutive blank symbols that may be present therein is compared to a threshold blank string length. Where a string of consecutive blank symbols in such an output is at least as long as the threshold blank string length (e.g., the string of blank symbols corresponding to the pause between the words “Hello” and “Please”), such a string of blank symbols may be deemed likely to correspond to a sentence pause. However, where a string of consecutive symbols in such an output is not at least as long as the threshold blank string (e.g., the strings of blank symbols between the words “Please” and “leave”, and between the words “leave” and “a”), such a string of blank symbols may be deemed to not correspond to a sentence pause. Thus, in the example depicted in FIG. 16B, the pause between the words “Hello” and “Please” may be deemed to be a likely sentence pause, and an indication thereof may be included in the candidate set 3118 c of likely sentence pauses.

In performing such comparisons of the lengths of strings of consecutive blank symbols to the threshold blank string length, an indication of the threshold blank string length may be retrieved from the configuration data 2335. In some embodiments, the threshold blank string length may have been previously derived during neural network training and/or testing to develop the neural network acoustic model configuration data included in the configuration data 2335 for use in configuring the neural network 2355 or 2555 to implement an acoustic model. During such training, it may be that portions of speech audio that are known to include pauses between sentences may be used, and the lengths of the resulting strings of blank symbols that correspond to those sentence pauses may be measured to determine what the threshold blank string length should be to enable its use in distinguishing pauses between sentences from at least pauses between words.

FIGS. 17A and 17B, taken together, illustrate an example of generating the converged set 3119 of likely sentence pauses. FIG. 17A illustrates the combining of multiple candidate sets 3118 of likely sentence pauses to generate the converged set 3119. FIG. 17B illustrates the use of the converged set 3119 in dividing the speech data set 3100 into data segments 3140 representing segments of the speech audio of the speech data set 3100.

As has been discussed in reference to FIGS. 15A-C and in reference to FIGS. 16A-B, it may be that, during pre-processing to divide speech audio represented by a speech data set 3100 into segments, multiple segmentation techniques may be used at least partially in parallel. As was also discussed, such parallelized performances may be distributed across multiple ones of the node devices (e.g., the node devices 2300 a and 2300 c of FIGS. 15A-C and FIGS. 16A-B, respectively), or across multiple execution threads associated with multiple processor cores 2551 of processor(s) 2550 of the control device 2500. Regardless of the exact manner in which the parallelized performances of multiple segmentation techniques is effectuated, the resulting multiple candidate sets 3118 of likely sentence pauses (e.g., the candidate sets 3118 a and 3118 c) may then be combined to generate the single converged set 3119 of likely sentence pauses that is used as the basis for effectuating the segmentation of the speech data set 3100 into data segments 3140.

Turning to FIG. 17A, in executing an aggregation component 2518 of the control routine 2510, core(s) of a processor 2550 of the control device 2500 may be caused to combine the candidate set 3118 a of likely sentence pauses generated using the APA segmentation technique of FIGS. 15A-C, and the candidate set 3118 c of likely sentence pauses generated using the CTC segmentation technique of FIGS. 16A-B, to generate the converged set 3119 of likely sentence pauses. As has been discussed, and as depicted with dotted lines, each of such multiple segmentation techniques may, in some embodiments, be performed within a different node device 2300 (e.g., the depicted node devices 2300 a and 2300 c).

As previously discussed, a variety of different approaches may be used in performing such a combining of multiple candidate sets 3118, including approaches to combining in which different segmentation techniques may be assigned relative weighting factors. As depicted, and as also previously discussed, such relative weight factors may be made dynamically adjustable based on one or more characteristics of the speech audio represented by the speech data set 3100. As further previously discussed in connection with the APA segmentation technique of FIGS. 15A-C, it may be that measurement(s) are made of audio noise level together with the measurements of peak amplitude that are performed as part of the APA segmentation technique.

Regardless of the exact manner in which the indication of audio noise level 2235 is generated, as depicted in FIG. 17A, such an indication may be used as an input for dynamically adjusting such relative weighting factors to take into account the relative degrees of susceptibility of each segmentation technique to being adversely affected by audio noise present in the speech audio. By way of example, it may be that the CTC segmentation technique is less susceptible to audio noise than the APA segmentation technique such that the presence of a higher level of audio noise in the speech audio may cause the candidate set 3118 c generated via the CTC segmentation technique to be given a greater relative weight compared to the candidate set 3118 a generated via the APA segmentation technique.

Turning to FIG. 17B, in executing a division component 2541 of the control routine 2540, core(s) of processor(s) 2550 of the control device 2500 may be caused to divide the speech data set 3100 into data segments 3140 based on the converged set 3119 of likely sentence pauses. In so doing, the speech audio represented by the speech data set 3100 may be divided into segments where the divisions between each adjacent pair of segments is caused to occur at the midpoints of each of the likely sentence pauses indicated in the converged set 3119. As a result, each of the segments of speech audio should be at least more likely to start and end with portions of sentence pauses, thereby serving to increase the likelihood that the entirety of the pronunciation of each letter, of each word, and/or of each sentence is fully contained within a single one of the segments, instead of being split across the divide between two segments. In this way, the accuracy of subsequent processing operations to detect acoustic features, to identify letters, and then to identify whole words, may be improved.

As also depicted, in embodiments that include the multiple node devices 2300 (e.g., the distributed processing system 2000 of FIG. 13A), the speed of such subsequent processing may be enhanced by distributing the data segments 3140 among the node device 2300 to enable at least partially parallel performances of such subsequent processing operations across multiple node devices 2300. Alternatively (and not specifically shown), it may be that such a similar enhancement in such processing may be achieved by distributing the data segments 3140 across multiple threads of execution of multiple cores 2551 of processor(s) 2550 of the control device 2500.

FIGS. 18A, 18B, 18C and 18D, taken together, illustrate an example of using the data segments 3140 into which the speech data set 3100 was divided to perform speech-to-text processing operations. FIGS. 18A-C, taken together, illustrate the use of a segment of speech audio represented by a data segments 3140 as input to the performance of feature detection as part of using an acoustic model to identify set(s) of candidate words in anticipation of generating corresponding set(s) of candidate n-grams. FIG. 18D illustrates the use of a set of candidate n-grams with a language model identify a next word most likely spoken for inclusion in a transcript represented by a text data set 3700.

Turning to FIGS. 18A-B, in executing a division component 2341 of the control routine 2340, either core(s) 2351 of a processor 2350 of a node device 2300 c, or core(s) 2551 of a processor 2550 of the control device 2500 may be caused to divide a data segment 3140 into multiple data frames 3140. In so doing, an indication of the length of the speech audio that is to be represented by each data frame 3140 may be retrieved from the configuration data 2335.

As depicted in FIG. 18A, and in comparing FIG. 18A to FIG. 16A, it may be that feature detection and use of an acoustic model may be repeated. Indeed, in comparing FIG. 18A to FIG. 16A, it becomes evident that the very same acoustic model based on a neural network (e.g., the neural network 2355 or 2555 incorporating the CTC output 2356 or 2556, respectively) may be used, again. However, it should be noted that other embodiments are possible in which different acoustic models based on other types of neural network may be used, and/or in which different acoustic models based on entirely different technologies may be used. In embodiments in which a neural network 2355 or 2555 is used, execution of a configuration component 2345 may cause core(s) 2351 of processor(s) 2350 or core(s) 2551 of processor(s) 2550 to so configure the neural network 2355 or 2555, respectively, to implement an acoustic model.

Regardless of whether the acoustic models of FIGS. 16A and 18A are identical, there are significant differences in the manner in which they are used. Unlike the use of an acoustic model in FIG. 16A to perform part of the CTC-based segmentation technique of FIGS. 16A-B, the acoustic model in FIG. 18A is used to used to perform part of speech-to-text processing operations. More specifically, the acoustic model is now used to generate, from a segment represented by a data segment 3140, a probability distribution set 3143. Each of the probability distributions within the set 3143 specifies, for a particular time within the segment, the relative probabilities for each of a pre-selected set of graphemes.

As will be familiar to those skilled in the art, over time, a number of different systems of notation have been devised for describing speech sounds for one or more languages using graphemes. In many of such notation systems, the graphemes may be text characters and/or similar visual symbols (e.g., text characters modified to include various accent markings). In different ones of such notation systems, at least some of the graphemes may each correspond to one or more phonemes, and/or at least some of the graphemes must be used in various combinations that each correspond to one or more phonemes. Thus, in specifying relative probabilities of a pre-selected set of graphemes, each probability distribution may specify the relative probabilities that each of a pre-selected set of speech sounds was uttered at a particular time within a segment of speech audio.

More specifically, and turning to FIG. 18B-C, as previously discussed, each speech segment (each of which is represented in storage by a corresponding data segment 3140) may be formed by dividing the speech audio of a speech data set 3100 at midpoints amidst what are determined to be likely sentence pauses. Each speech segment may then be further divided into frames (each of which is represented in storage by a corresponding data frame 3141), which are kept in temporal order. Thus, as depicted in FIG. 18B, the speech segment (again, represented by a data segment 3140) that corresponds to the depicted probability distribution set 3143 may begin with a first few consecutive speech frames (each of which is represented by a corresponding data frame 3141) in which there may be no speech sounds, as would be expected within a likely sentence pause. As a result, each of the corresponding first multiple consecutive probability distributions 3144 (including the earliest thereof) may indicate that a grapheme (e.g., a text character and/or a blank symbol) for an empty space has the highest probability of having occurred within the corresponding speech frame.

Following such consecutive probability distributions 3144 associated with the likely sentence pause, there may then be the first of multiple consecutive probability distributions 3144 that may be associated with the pronunciation of the letters of the first word of a sentence (the transition from probability distributions 3144 associated with a likely sentence pause to probability distributions 3144 that may be associated with pronouncing a word is marked by vertical dashed line). In executing a candidate word component 2545 of the control routine 2540, processor(s) 2550 of the control device 2500 may, based on those multiple consecutive probability distributions 3144, derive a pre-selected quantity of candidate words 3145 that are each among the most likely to be the first word that was spoken throughout the corresponding consecutive speech frames. The processor(s) 2550 may then be caused by execution of a candidate n-gram component 2546 to convert the set of candidate words 3145 into a candidate n-gram set 3146 by adding up to a pre-selected quantity of words that were previously identified as the immediately preceding words in what may be a sentence that corresponds to the probability distribution set 3143. However, since each of the candidate words 3145 is preceded by what is deemed to be a likely sentence pause, there may be no such preceding words to be added such that the resulting candidate n-gram set 3146 contains a set of uni-grams that are each just one of the candidate words 3145.

FIG. 18B also depicts another example set of candidate words 3145 being derived from multiple consecutive probability distributions 3144 at a temporally later location within the depicted probability distribution set 3143 that may be associated with pronouncing another word at a later time within the same speech segment. Again, in executing the candidate word component 2545, processor(s) 2550 of the control device 2500 may, based on those multiple consecutive probability distributions 3144, derive another pre-selected quantity of candidate words 3145 that are each among the most likely to be the word that was spoken throughout these other corresponding multiple consecutive speech frames.

The processor(s) 2550 may then be caused by execution of a candidate n-gram component 2546 to convert this other set of candidate words 3145 into another candidate n-gram set 3146 by adding up to the pre-selected quantity of words that were previously identified as the immediately preceding words in what may be a sentence that corresponds to the probability distribution set 3143. Unlike the previously discussed set of candidate words 3145, there may be multiple immediately preceding words that were spoken up to the point at which one of the candidate words 3145 within this other set of candidate words 3145. Therefore, the other candidate n-gram set 3146 may include up to the pre-selected quantity of words.

Turning to FIG. 18D, regardless of whether the n-grams within a candidate n-gram set 3146 generated within the control device 2500 include any immediately preceding words ahead of the candidate words 3145 thereof, in executing a beam search component 2347, core(s) 2351 of processor(s) 2350 or core(s) 2551 of processor(s) 2550 may be caused to perform a beam search within the corpus data set 3400 for one or more of the n-grams present within the candidate n-gram set 3146. As will be familiar to those skilled in the art of n-gram language models, each n-gram within an n-gram corpus may be accompanied therein with an indication of the relative frequency of its occurrence and/or its relative probability of occurrence within texts of a particular language (based on the sample texts of the particular language used in generating the n-gram corpus). As each n-gram is found within the corpus data set 3400, an indication of the relative probability of that n-gram occurring may be stored within a probability set 3147 generated for all of the candidate n-grams in the candidate n-gram set 3146.

Following generation of the probability set 3147, execution of a transcript component 2548 may cause processor(s) 2550 of the control device 2500 to, based on the indications of the relative probabilities in the probability set 3147 for each n-gram within the candidate n-gram set 3146, identify a candidate word 3145 among the corresponding set of candidate words 3145 as the word that was most likely the next word to be spoken. The identified most likely spoken word may then be added to the transcript of the speech audio represented as a text data set 3700.

FIGS. 19A and 19B illustrate examples of additional improvements that may be incorporated to the generation of transcripts. FIG. 19A illustrates aspects of the addition of dynamic per-word assignment of relative weighting to the use of an acoustic model or a language in identifying spoken words. FIG. 19B illustrates aspects of selective concatenation of segments of audio speech to effect the formation of longer transcripts to improve the results of subsequent post-processing text analysis operations.

Turning to FIG. 19A, as previously discussed, it has become commonplace to employ a two-stage combination of an acoustic model and a language model in which the acoustic model is typically relied upon to perform a first pass at identifying words that are likely to be the ones that were spoken, and the language model is typically relied upon to perform the next and final pass by refining the identification of such spoken words such that the words identified by the language model are the ones from which a transcript is generated. However, and as also previously discussed, the reduction in error rate achieved by such a two-stage combination is still widely seen as being too high. Again, a possible reason for being still too high is that a good language model tends to resist identifying words that are actually spoken where those spoken words include mistakes in vocabulary and/or syntax.

To improve upon the error rate of such typical two-stage use of a combination of an acoustic model and a language model, in some embodiments, the transcript component 2548 may incorporate additional functionality to dynamically vary the relative weighting assigned to each of the acoustic model and the language model for each word to be identified based on the degree of uncertainty in the per-grapheme probability distributions output by the acoustic model for each word. Thus, in addition to being provided with the probability set 3147 and corresponding candidate words 3145 associated with a segment of speech audio as inputs, the transcript component 2548 may additionally receive the corresponding probability distribution set 3143 that includes the corresponding probability distributions for graphemes associated with the same segment of speech audio.

In executing the transcript component 2548, core(s) 2551 of processor(s) 2550 of the control device 2500 may be caused to the probability distributions of graphemes that are output by the acoustic model for the pronunciation of a single word spoken within the segment to derive a measure of the degree of uncertainty for each of those probability distributions. Such a degree of uncertainty may be based on degree of a perplexity, degree of entropy, other statistical measures of those probability distributions. Again, such a degree of uncertainty may serve as an indication of the degree to which a probability distribution for a grapheme presents an indefinite indication of which speech sound was uttered during a corresponding portion of the segment of speech audio.

A probability distribution for graphemes that provides an uncertain indication of what speech sound was uttered may be one in which the degree of probability for the grapheme indicated as being the most probable is not significantly higher than the degree of probability for the grapheme indicated as being the second most probable. More specifically, where the difference between these two degrees of probability is less than a pre-determined threshold difference in probabilities, the probability distribution may be deemed to provide an indication that the second most probable grapheme is almost as likely to describe a speech sound that was uttered as the speech sound described by the most probable grapheme such that it is deemed to be uncertain as to which of these two speech sounds is the one that was uttered.

In this way, the probability distribution may be said to provide an ambiguous indication of what speech sound was uttered. In some embodiments, the degree of uncertainty used to control which model is to be relied upon to identify a single word may be derived from measures of such a difference in probabilities associated with the most probable grapheme and the second most probable grapheme within each probability distribution associated with the single word. These differences in probabilities may be averaged or otherwise aggregated to derive a single value indicative of the degree of uncertainty, which may then be compared to a threshold degree of uncertainty specified in the configuration data 2335. Where the degree of uncertainty is less than the threshold, greater weight may be assigned to the identification of the single word using the acoustic model, and where the degree of uncertainty is greater than the threshold, greater weight may be assigned to the identification of the single word using the language model.

In other embodiments, the degree of uncertainty used to control which model is to be relied upon to identify a single word may be derived as an aggregate degree of perplexity or entropy. Stated differently, the degree of uncertainty may be based on calculations of the degree of entropy or degree of perplexity (which may be derived from a degree of entropy) of each probability distribution associated with the single word may be calculated and aggregated to derive a degree of uncertainty. In such embodiments, the aggregated degree of uncertainty may be compared to a threshold degree of uncertainty specified in the configuration data 2335. Again, where the degree of uncertainty is less than the threshold, greater weight may be assigned to the identification of the single word using the acoustic model, and where the degree of uncertainty is greater than the threshold, greater weight may be assigned to the identification of the single word using the language model.

As previously discussed, in some embodiments, both of the acoustic model and the language model may always be utilized in combination for each spoken word, regardless of whether the dynamic per-word determination is made to give greater weight to relying more on the acoustic model or the language model to identify a word. Thus, the beam searches associated with the execution of the beam search component 2347 to use the language model (where the language model is based on an n-gram corpus) may always be performed regardless of such dynamic per-word assignment of relative weighting. This may be the case where an output of the language model is employed as an input to the dynamic per-word relative weighting assigned to the acoustic and language models in addition to degree of uncertainty for the probability distributions for the corresponding graphemes.

Alternatively, in other embodiments, it may be that the language model is not used to provide any input to the dynamic per-word relative weighting. In such other embodiments, such a situation may provide the opportunity to entirely refrain from consuming processing and or storage resources to perform beam searches associated with using the language model to identify a particular word if the results of the dynamic per-word relative weighting are such that the identification of the word that would be provided by the language model will not be used. In this way, use of the language model may be made contingent on such dynamic per-word relative weighting.

As will be familiar to those skilled in the art, speech recognition in the human brain involves using a combination of detecting and recognizing speech sounds as received by the ears, and recognizing portions of language based on language rules. It has been observed that, where speech sounds are able to be clearly heard, speech recognition in the human brain tends to rely more heavily on those sounds to determine what was said. However, such reliance on speech sounds as received by the human ears may become insufficient where acoustic conditions are such that some speech sounds are masked enough to not be heard such that there are noticeable gaps in the speech sounds as received. It has been observed that, where at least some speech sounds are less clearly heard, speech recognition in the human brain tends to rely more heavily on language rules to determine what was said, thereby effectively “filling in the gaps” among the speech sounds that were able to be heard. To put this more simply, it has been observed that the human brain will take advantage of opportunities to not expend the resources needed to use language rules for such purposes when it is not necessary.

Turning to FIG. 19B, from experimentation and observation, it has been found that, generally, many forms of automated text analyses are able to be more successfully used with longer transcripts. Again, it has been found that shorter transcripts tend to cause an overemphasis on words with greater frequencies of use in a language, with the result that analyses to derive topics and/or other insights concerning the text of a transcript tend to produce less useful results.

As an approach to counteracting this effect, in some embodiments, all of the text derived from a single piece of speech audio may be maintained and treated (at least for purposes of performing text analyses) as a single transcript. More specifically, the text generating from speech-to-text processing may be organized within the text data set 3700 as a single transcript. However, as also previously discussed, a single transcript encompassing speech audio that is especially long and/or that includes multiple conversations and/or verbal presentations may also beget less useful results when text analyses are performed thereon.

Thus, in some embodiments, rules concerning lengths of transcripts, frequencies of words, and/or acoustic features such as relatively lengthy pauses may be used to bring about the generation of lengths and/or quantities of transcripts for each piece of speech audio that are more amenable to providing useful results from automated text analyses. More specifically, a set of such rules may be used to cause the selective concatenation of the text of consecutive sets of segments of speech audio to form multiple transcripts from a piece of speech audio that may be stored together as a set of transcripts within a single text data set 3700. Such a text data set 3700 may include indications of the relative temporal order of the multiple transcripts to preserve at least that contextual aspect.

Indications of such rules and/or thresholds therefore may be maintained as part of the configuration data 2335. Among such thresholds may be a minimum and/or maximum threshold for the size of a transcript, which may be expressed in terms of quantities of words and/or lengths of time periods. In some of such embodiments, it may be that text associated with segments of speech audio may be automatically combined to form transcripts that have a length that meets such word count and/or time thresholds.

Alternatively or additionally, the configuration data 2335 may specify a minimum threshold quantity of words in a transcript that are required to have a frequency of occurrence in a language that falls below a specified maximum threshold. In some of such embodiments, it may be that text associated with segments of speech audio may be combined to form transcripts in which the combination of words includes such a requisite quantity of such lower frequency words. In so doing, the storage, within a corpus data set 3400, of uni-grams that are each correlated to an indication of frequency of use may be relied upon as a source of such indications of frequency.

Also alternatively or additionally, the configuration data 2335 may specify a minimum threshold length of time for a pause between speech sounds that may be greater than the minimum threshold length for a likely sentence pause such that it may be deemed a likely pause between conversations and/or verbal presentations where a change of subject may be more likely to occur. In some of such embodiments, occurrences of such longer pauses may be used as breakpoints at which text may be divided to define multiple transcripts. There may still be an enforcement of minimum and/or maximum thresholds as a default to address situations in which too few or too many of such longer pauses are found to occur.

FIGS. 20A, 20B, 20C, 20D and 20E, together, illustrate an example embodiment of a logic flow 4100. The logic flow 4100 may be representative of some or all of the operations executed by one or more embodiments described herein. More specifically, the logic flow 4100 may illustrate operations performed by core(s) 2351 and/or 2551 of the processor(s) 2350 and/or 2550 of the node devices 2300 and/or of the control device 2500, respectively, in executing various ones of the control routines 2310, 2340, 2510 and 2540.

Starting at FIG. 20A, at 4110, processor(s) of a control device of a processing system (e.g., the processor(s) 2550 of the control device 2500 of the processing system 2000 of either of FIG. 13A or 13B) may receive a request from a requesting device via a network (e.g., the requesting device 2700 via the network 2999) to perform speech-to-text conversion of speech audio represented by a specified speech data set (e.g., one of the speech data sets 3100).

At 4112, pre-processing of the speech audio represented by the specified speech data set may begin with either a processor of the control device or processor(s) of one or more node devices of the processing system (e.g., one or more of the node devices 2300) dividing the speech data set into data chunks that each represent a chunk of the speech audio. As has been discussed, the pre-processing may entail the performances of multiple segmentation techniques (e.g., the combination of at least the APA segmentation technique of FIGS. 15A-C, and the CTC segmentation technique of FIGS. 16A-B) at least partially in parallel. As also discussed, where the processing system does include multiple node devices (e.g., the multiple node devices 2300), it may be that each segmentation technique is assigned to be performed by a different one of the node devices. Alternatively, where the processing system does not so include such a multitude of node devices, it may be that each segmentation technique is assigned to be performed by a different core and/or a different processor of the control device.

It should again be noted that the chunks of the speech audio used by different ones of the segmentation techniques may not be of the same size, or more precisely, may not represent chunks of the speech audio that are of the same length (e.g., as previously discussed, the chunks of speech audio generated for the APA segmentation technique may be shorter than those generated for the CTC segmentation technique). Therefore, it may be that multiple different sets of chunks of the speech audio are generated at 4112. More precisely, where each segmentation technique is assigned to a different node device or to a different thread of execution, it may be that the division of the speech audio into chunks is among the operations that are also so assigned such that separate node devices or separate cores are used to separately generate chunks of speech audio that are of appropriate length for their corresponding one of the segmentation techniques.

Regardless of the exact manner in which chunks of speech audio are generated at 4112, as depicted, multiple portions of pre-processing may be performed at least partially in parallel across FIGS. 20B, 20C and 20D, including the APA and CTC segmentation techniques.

Turning to FIG. 20B, and following the generation of APA data chunks at 4112 that are of appropriate size for use as inputs to the APA segmentation technique (e.g., the data chunks 3110 a), at 4120, core(s) of a processor of either a node device or of the control device may analyze the chunk of speech audio represented by each APA data chunk to identify and measure the peak amplitude present therein. At 4122, with the peak amplitudes of each of the APA data chunks so measured, a pre-selected percentile amplitude may be derived from across all of the measured peak amplitudes from across all of the APA data chunks, and may be designated to serve as a threshold amplitude (e.g., the threshold amplitude 2232).

At 4124, the peak amplitude measured within each of the APA data chunks may be compared to the threshold amplitude. At 4126, each APA data chunk representing a chunk of speech audio having a peak amplitude greater than the threshold amplitude may be designated as a speech data chunk (e.g., a speech data chunk 3110 s), and each APA data chunk representing a chunk of speech audio having a peak amplitude less than the threshold amplitude may be designated as a pause data chunk (e.g., a pause data chunk 3110 p). Again, in various differing embodiments, each APA data chunk representing a chunk of speech audio having a peak amplitude equal to the threshold amplitude may be designated as either a speech data chunk or a pause data chunk.

At 4130, a first set of temporally consecutive APA data chunks of a pre-selected quantity, starting with the temporally earliest one of the APA data chunks, may be selected and analyzed to identify the longest consecutive subset of the APA data chunks therein that have been designated as pause data chunks, thereby corresponding to the longest pause present across all of the corresponding consecutive chunks of speech audio represented by the set of APA data chunks. The identified longest pause may be designated a likely sentence pause.

At 4132, an indication of the just-designated likely sentence pause may then be noted within an APA candidate set of likely sentence pauses (e.g., the APA candidate set 3118 a of likely sentence pauses). As previously discussed, such an indication of a likely sentence pause within the APA candidate set may include an indication of the temporal location of the likely sentence pause within the entirety of the speech audio.

At 4134, a check may be made of whether there are any more APA data chunks beyond (i.e., temporally later than) the set of APA data chunks just analyzed. If so, then at 4136, another set of temporally consecutive APA data chunks of a pre-selected quantity may be selected, where the newly selected set may start either 1) with the APA chunk that temporally follows the subset of APA data chunks that make up the longest pause of the last set, or 2) amidst the subset of APA data chunks that make up the longest pause of the last set (e.g., with the APA chunk at the midpoint of that longest pause). The newly selected set of APA data chunks may then be analyzed to identify the longest consecutive subset of the APA data chunks with the new set that have been designated as pause data chunks, thereby corresponding to the longest pause present across all of the corresponding consecutive chunks of speech audio represented by the set of APA data chunks. The identified longest pause may be designated a likely sentence pause. Again, at 4132, an indication of the just-designated likely sentence pause may then be noted within the APA candidate set of likely sentence pauses.

However, if at 4134, there are no more APA data chunks beyond the set of APA data chunks just analyzed, then a combining of multiple candidate sets of likely sentence pauses may be performed at 4170 and 4172 in FIG. 20E, as will shortly be described.

Turning to FIG. 20C, and following the generation of APA data chunks at 4112 that are of appropriate size for use as inputs to the APA segmentation technique (e.g., the data chunks 3110 a), at 4140, core(s) of a processor of either a node device or of the control device may analyze the chunk of speech audio represented by each APA data chunk to identify and measure an amplitude of audio noise present therein. As previously discussed in reference to FIG. 15A, it may be that such measurements of a level of audio noise may be taken coincident with the taking of measurements of peak amplitude of each of the APA data chunks. However, it should be noted that other embodiments are possible in which measurements of a level of audio noise may be taken of other chunks generated for another of the multiple segmentation techniques, or measurement(s) may be taken of a level of audio noise in the speech audio at a time and/or in a manner that may entirely unconnected with any of the segmentation techniques.

At 4142, with the audio noise levels of each of the APA data chunks so measured, at least one indication of the audio noise level within the speech audio (e.g., the audio noise level 2235) may be derived using any of a variety of ways. By way of example, and as previously discussed, such an indicated audio noise level may be based on average noise levels, lowest noise levels, and/or highest noise levels across all of the APA data chunks.

Following the derivation of the indicated audio noise level, a combining of multiple candidate sets of likely sentence pauses may be performed at 4170 and 4172 in FIG. 18E, as will shortly be described, including the use of the indicated audio noise level.

Turning to FIG. 20D, and following the generation of CTC data chunks at 4112 that are of appropriate size for use as inputs to the CTC segmentation technique (e.g., the data chunks 3110 c), at 4150, core(s) of a processor of either a node device or of the control device may configure a neural network of the node device or of the control device to implement an acoustic model. As has been discussed, the neural network that is so configured may incorporate a CTC output that would normally be used to output a blank symbol that provides an indication of their being consecutive instances of a character that are not to be merged. At 4152, the temporally earliest one of the CTC data chunks may be provided to the neural network as an input.

At 4154, if there are no strings of consecutive blank symbols output by the neural network, then a check may be made at 4164 of whether there are any more CTC data chunks remaining to be provided to the neural network as input. If there is at least one more of such CTC data chunks remaining, then the temporally next CTC data chunk (i.e., the next CTC data chunk in order from the temporally earliest to the temporally latest) may be provided to the neural network as input at 4166.

However, if at 4154, there are one or more strings of consecutive blank symbols output by the neural network in response to the provision thereto of a CTC data chunk as input, then at 4156, the length of each of those one or more strings may be compared to a pre-determined threshold blank string length. At 4158, any string of consecutive blank symbols that is at least as long as the threshold blank string length, then each such string may be designated as a likely sentence pause. If, at 4160, there are no strings of consecutive blank symbols in the output of the neural network that have been so designated as likely sentence pauses, then the check of whether there are any more CTC data chunks remaining may be made at 4164. However, if at 4160, there are one or more strings of consecutive blank symbols that have been designated as likely sentence pauses, then for each such string, an indication of a likely sentence pause may then be noted within the CTC candidate set of likely sentence pauses at 4162, and then the check may be made at 4164 for more CTC data chunks.

However, if at 4164, there are no more CTC data chunks, then a combining of multiple candidate sets of likely sentence pauses may be performed at 3170 and 3172 in FIG. 20E, as will now be described.

Turning to FIG. 20E, at 4170, core(s) of a processor of either a node device or of the control device may assign relative weighting factors to each of the segmentation techniques by which a candidate set of likely sentence pauses has been generated. As has been discussed, such weighting factors may be made dynamically adjustable based on the earlier derived indication of audio noise level, and this may be done in recognition of the differing degrees to which each of the segmentation techniques is susceptible to the presence of audio noise within speech audio. At 4172, the assigned relative weighting factors may be used in the combining of the multiple candidate sets of likely sentences pauses to generate the converged set thereof.

At 4180, core(s) of a processor of each of one or more node devices, and/or cores(s) of a processor of the control device may re-divide the speech data set into data segments that each represent a segment of the speech audio. With the provision of segments of the speech audio to use as an input, the processing operations to perform the requested speech-to-text may begin. As has been discussed, due to the performance of the pre-processing operations, each point at which the speech audio is divided to form segments is at least likely to be a midpoint of a sentence pause, thereby making it more likely that each segment will fully contain the complete pronunciations of phonemes, words and/or entire sentences. As also discussed, it may be that the segments are distributed among multiple node devices or among multiple execution threads within the control device to enhance the speed at which such processing is performed.

At 4182, feature detection is performed on each segment to detect instances of a pre-selected set of acoustic features that are to be provide as an input to an acoustic model for purposes of identifying likely text characters. At 4184, within each node device and/or within the control device, core(s) of a processor may configure neural network(s) to implement an acoustic model for use in character identification. Again, the same type of neural network with CTC output may be configured to re-implement the same acoustic model as was used during pre-processing in the CTC segmentation technique.

At 4186, each data segment is provided to such a neural network as input for the identification of likely text characters (along with blank symbols used to identify instances of identical consecutive text characters). At 4188, such identified text characters are provided to implementation(s) of a language model as input for the identification of words.

At 4190, a processor of a node device or a processor of the control device may assemble the identified words, in temporal order, to form text data that represents the text into which the speech audio of the speech data set has been converted (e.g., the text data 2519). As previously discussed, such text data may then be transmitted back to the device from which the request was received to perform the speech-to-text conversion.

FIG. 21 illustrates an example embodiment of another logic flow 4200. The logic flow 4200 may be representative of some or all of the operations executed by one or more embodiments described herein. More specifically, the logic flow 4200 may illustrate operations performed by core(s) 2351 and/or 2551 of the processor(s) 2350 and/or 2550 of the node devices 2300 and/or of the control device 2500, respectively, in executing various ones of the control routines 2340 and 2540.

At 4210, core(s) of processor(s) of a node device of a processing system (e.g., the core(s) 2351 of the processor(s) 2350 of one of the node devices 2300 of the processing system 2000 of either of FIG. 13A or 13B), or core(s) of processor(s) of a control device of the processing system (e.g., the core(s) 2551 of the processor(s) 2550 of the control device 2500 of the processing system 2000 of either of FIG. 13A or 13B) may perform feature detection on one or more consecutive frames of a segment of speech audio covering a period of time during which a next word was spoken. As has been discussed, the output of the performance of feature detection may be data structures (e.g., the feature vectors 3142) that provide indications of detected instances of various acoustic features, along with indications of when those instances occurred.

At 4212, such feature vectors generated from the performance of feature detection may be provided as input to an acoustic model. As has been discussed, the acoustic model may be implemented using a neural network (e.g., the neural network 2355 or 2555, which may include a CTC output 2356 or 2556, respectively), or using any of a variety of other technologies.

At 4214, the core(s) of the processor(s) of either the node device or the control device may be caused to use the acoustic model with the feature vectors as input to generate corresponding probability distributions of graphemes. As has been discussed, each grapheme may be correlated, either individually or in various combinations, to one or more speech sounds. As a result, each of the probability distributions provides an indication of relative probabilities of various different speech sounds having been uttered at a particular time.

At 4216, from multiple probability distributions that are associated with the pronunciation of the next single word that was spoken and that is to be identified for addition to a transcript, a set of a pre-determined quantity of candidate words (e.g., the candidate words 3145) may be generated, where each of the candidate words is among those that are most likely to be the next spoken word. At 4220, for each candidate word in the set of candidate words, a corresponding candidate n-gram may be generated that is to become part of a corresponding set of candidate n-grams (e.g., the set 3146 of candidate n-grams).

At 4222, the core(s) of the processor(s) of either the node device or the control device may be caused to use the language model with the set of candidate n-grams as input to generate a corresponding set of probabilities (e.g., one of the probability sets 3147). As has been discussed, where the language model is based on an n-gram corpus (e.g., one of the corpus data sets 3400), beam searches may be used to retrieve the per-n-gram probabilities stored as part of the n-gram corpus. As a result, each of the probability sets provides the relative probabilities of the set of n-grams, thereby enabling the most probable candidate n-gram of that set to be determined, and in so doing, enabling the most probable corresponding candidate word to be identified as the next most likely word to be spoken, according to the language model.

At 4230, each of the probability distributions for graphemes associated with the next word may be analyzed to derive an aggregate degree of uncertainty for those probability distributions. If, at 4232, the resulting degree of uncertainty is greater than a pre-determined threshold level, then at 4234, greater weighting may be given to relying on the language model to identify the next word most likely to have been spoken. However, if at 4232, the resulting degree of uncertainty is less than the pre-determined threshold level, then at 4236, greater weighting may be given to relying on the acoustic model to identify the next word most likely to have been spoken.

In various embodiments, each of the processors 2350, 2550 and 2750 may include any of a wide variety of commercially available processors. Further, one or more of these processors may include multiple processors, a multi-threaded processor, a multi-core processor (whether the multiple cores coexist on the same or separate dies), and/or a multi-processor architecture of some other variety by which multiple physically separate processors are linked.

However, in a specific embodiment, the processor(s) 2350 of each of the one or more node devices 2300 may be selected to efficiently perform the analysis of multiple instances of pre-processing, processing and/or post-processing operations at least partially in parallel. By way of example, the processors 2350 may incorporate a single-instruction multiple-data (SIMD) architecture, may incorporate multiple processing pipelines, and/or may incorporate the ability to support multiple simultaneous threads of execution per processing pipeline. Alternatively or additionally by way of example, the processor 1550 may incorporate multi-threaded capabilities and/or multiple processor cores to enable parallel performances of the tasks of more than job flow.

In various embodiments, each of the control routines 2310, 2340, 2370, 2510, 2540, 2570 and 2740, including the components of which each is composed, may be selected to be operative on whatever type of processor or processors that are selected to implement applicable ones of the processors 2350, 2550 and/or 2750 within each one of the devices 2300, 2500 and/or 2700, respectively. In various embodiments, each of these routines may include one or more of an operating system, device drivers and/or application-level routines (e.g., so-called “software suites” provided on disc media, “applets” obtained from a remote server, etc.). Where an operating system is included, the operating system may be any of a variety of available operating systems appropriate for the processors 2350, 2550 and/or 2750. Where one or more device drivers are included, those device drivers may provide support for any of a variety of other components, whether hardware or software components, of the devices 2300, 2500 and/or 2700.

In various embodiments, each of the storages 2360, 2560 and 2760 may be based on any of a wide variety of information storage technologies, including volatile technologies requiring the uninterrupted provision of electric power, and/or including technologies entailing the use of machine-readable storage media that may or may not be removable. Thus, each of these storages may include any of a wide variety of types (or combination of types) of storage device, including without limitation, read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data-Rate DRAM (DDR-DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory (e.g., ferroelectric polymer memory), ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, one or more individual ferromagnetic disk drives, non-volatile storage class memory, or a plurality of storage devices organized into one or more arrays (e.g., multiple ferromagnetic disk drives organized into a Redundant Array of Independent Disks array, or RAID array). It should be noted that although each of these storages is depicted as a single block, one or more of these may include multiple storage devices that may be based on differing storage technologies. Thus, for example, one or more of each of these depicted storages may represent a combination of an optical drive or flash memory card reader by which programs and/or data may be stored and conveyed on some form of machine-readable storage media, a ferromagnetic disk drive to store programs and/or data locally for a relatively extended period, and one or more volatile solid state memory devices enabling relatively quick access to programs and/or data (e.g., SRAM or DRAM). It should also be noted that each of these storages may be made up of multiple storage components based on identical storage technology, but which may be maintained separately as a result of specialization in use (e.g., some DRAM devices employed as a main storage while other DRAM devices employed as a distinct frame buffer of a graphics controller).

However, in a specific embodiment, the storage 2560 in embodiments in which the one or more of the federated devices 2500 provide federated spaces 2566, or the storage devices 2600 in embodiments in which the one or more storage devices 2600 provide federated spaces 2566, may be implemented with a redundant array of independent discs (RAID) of a RAID level selected to provide fault tolerance to objects stored within the federated spaces 2566.

In various embodiments, the input device 2720 may be any of a variety of types of input device that may each employ any of a wide variety of input detection and/or reception technologies. Examples of such input devices include, and are not limited to, microphones, remote controls, stylus pens, card readers, finger print readers, virtual reality interaction gloves, graphical input tablets, joysticks, keyboards, retina scanners, the touch input components of touch screens, trackballs, environmental sensors, and/or either cameras or camera arrays to monitor movement of persons to accept commands and/or data provided by those persons via gestures and/or facial expressions.

In various embodiments, the display 2780 may be any of a variety of types of display device that may each employ any of a wide variety of visual presentation technologies. Examples of such a display device includes, and is not limited to, a cathode-ray tube (CRT), an electroluminescent (EL) panel, a liquid crystal display (LCD), a gas plasma display, etc. In some embodiments, the display 2780 may be a touchscreen display such that the input device 2720 may be incorporated therein as touch-sensitive components thereof.

In various embodiments, each of the network interfaces 2390, 2590 and 2790 may employ any of a wide variety of communications technologies enabling these devices to be coupled to other devices as has been described. Each of these interfaces includes circuitry providing at least some of the requisite functionality to enable such coupling. However, each of these interfaces may also be at least partially implemented with sequences of instructions executed by corresponding ones of the processors (e.g., to implement a protocol stack or other features). Where electrically and/or optically conductive cabling is employed, these interfaces may employ timings and/or protocols conforming to any of a variety of industry standards, including without limitation, RS-232C, RS-422, USB, Ethernet (IEEE-802.3) or IEEE-1394. Where the use of wireless transmissions is entailed, these interfaces may employ timings and/or protocols conforming to any of a variety of industry standards, including without limitation, IEEE 802.11a, 802.11ad, 802.11ah, 802.11ax, 802.11b, 802.11g, 802.16, 802.20 (commonly referred to as “Mobile Broadband Wireless Access”); Bluetooth; ZigBee; or a cellular radiotelephone service such as GSM with General Packet Radio Service (GSM/GPRS), CDMA/1×RTT, Enhanced Data Rates for Global Evolution (EDGE), Evolution Data Only/Optimized (EV-DO), Evolution For Data and Voice (EV-DV), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), 4G LTE, 5G, etc.

However, in a specific embodiment, one or more of the network interfaces 2390 and/or 2590 may be implemented with multiple copper-based or fiber-optic based network interface ports to provide redundant and/or parallel pathways in exchanging at least the speech data sets 2130.

In various embodiments, the division of processing and/or storage resources among the federated devices 1500, and/or the API architectures employed to support communications between the federated devices and other devices may be configured to and/or selected to conform to any of a variety of standards for distributed processing, including without limitation, IEEE P2413, AllJoyn, IoTivity, etc. By way of example, a subset of API and/or other architectural features of one or more of such standards may be employed to implement the relatively minimal degree of coordination described herein to provide greater efficiency in parallelizing processing of data, while minimizing exchanges of coordinating information that may lead to undesired instances of serialization among processes. However, it should be noted that the parallelization of storage, retrieval and/or processing of portions of the speech data sets 2130 are not dependent on, nor constrained by, existing API architectures and/or supporting communications protocols. More broadly, there is nothing in the manner in which the speech data sets 2130 may be organized in storage, transmission and/or distribution via the network 2999 that is bound to existing API architectures or protocols.

Some systems may use Hadoop®, an open-source framework for storing and analyzing big data in a distributed computing environment. Some systems may use cloud computing, which can enable ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Some grid systems may be implemented as a multi-node Hadoop® cluster, as understood by a person of skill in the art. Apache™ Hadoop® is an open-source software framework for distributed computing. 

The invention claimed is:
 1. An apparatus comprising at least one processor and a storage to store instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receive, from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio; and in response to the request, the at least one processor is caused to perform speech-to-text processing operations comprising: use an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; derive at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyze the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; compare the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, the at least one processor is caused to select the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, the at least one processor is caused to select, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and add the next word most likely spoken to a transcript of the speech audio.
 2. The apparatus of claim 1, wherein: derivation of at least the first candidate word comprises deriving a set of candidate words most likely spoken in the speech audio from the first set of probabilities; the set of candidate words comprises: the first candidate word; and the second candidate word; and the second set of probabilities is generated by the language model to correspond to the set of candidate words.
 3. The apparatus of claim 2, wherein the at least one processor is caused, in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, to refrain from expending processing resources to use the language model to generate the second set of probabilities.
 4. The apparatus of claim 2, wherein the at least one processor is caused to select the first candidate word or the second candidate word based on the degree of uncertainty of the first set of probabilities in combination with at least a subset of the probabilities within the second set of probabilities.
 5. The apparatus of claim 2, wherein: the language model is based on an n-gram corpus comprising n-grams correlated to corresponding probabilities of use in a pre-selected language; and the use of the language model to generate the second set of probabilities comprises the at least one processor performing operations comprising: for each candidate word of the set of candidate words, generate a corresponding candidate n-gram of a set of candidate n-grams that comprises a combination of the candidate word and at least one preceding word spoken in the speech audio; for each candidate n-gram, search the n-gram corpus for the candidate n-gram to retrieve the corresponding probability for inclusion in the second set of probabilities; determine which candidate n-gram of the set of candidate n-grams corresponds to the highest probability among the second set of probabilities; and indicate the candidate word that corresponds to the candidate n-gram that corresponds to the highest probability of the second set of probabilities as being the second candidate word.
 6. The apparatus of claim 1, wherein: the acoustic model accepts indications of instances of acoustic features of a pre-selected set of acoustic features occurring within the portion of speech audio; and the use of the acoustic model to generate the first set of probabilities comprises the at least one processor performing operations comprising: use a feature detector to detect instances of occurrence of the pre-selected set of acoustic features to generate at least one feature vector that provides the indications of acoustic features occurring within the portion of speech audio; and provide the at least one feature vector to the acoustic model as input to generate the first set of probabilities.
 7. The apparatus of claim 6, wherein: the acoustic model is implemented with a neural network; and the neural network comprises a connectionist temporal classification (CTC) output to provide an indication of a pause between speech sounds of the next word for inclusion in the at least one feature vector.
 8. The apparatus of claim 1, wherein: the first set of probabilities comprises at least one probability distribution indicative of relative probabilities of utterance of each grapheme of a pre-selected set of graphemes at a time during pronunciation of speech sounds of the next word; and analysis of the first set of probabilities to derive the degree of uncertainty comprises deriving a degree of entropy or perplexity for each probability distribution of the at least one probability distribution.
 9. The apparatus of claim 8, wherein each grapheme of the pre-selected set of graphemes corresponds to a portion of a phoneme, a single phoneme or a combination of phonemes.
 10. The apparatus of claim 8, wherein: each grapheme of the pre-selected set of graphemes corresponds to a single text character of a pre-selected set of text characters or a combination of text characters of the pre-selected set of text characters; and the pre-selected set of text characters comprises a blank symbol indicative of a pause between speech sounds.
 11. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions operable to cause at least one processor to perform operations comprising: receive, from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio; and in response to the request, the at least one processor is caused to perform speech-to-text processing operations comprising: use an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; derive at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyze the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; compare the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, the at least one processor is caused to select the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, the at least one processor is caused to select, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and add the next word most likely spoken to a transcript of the speech audio.
 12. The computer-program product of claim 11, wherein: derivation of at least the first candidate word comprises deriving a set of candidate words most likely spoken in the speech audio from the first set of probabilities; the set of candidate words comprises: the first candidate word; and the second candidate word; and the second set of probabilities is generated by the language model to correspond to the set of candidate words.
 13. The computer-program product of claim 12, wherein the at least one processor is caused, in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, to refrain from expending processing resources to use the language model to generate the second set of probabilities.
 14. The computer-program product of claim 12, wherein the at least one processor is caused to select the first candidate word or the second candidate word based on the degree of uncertainty of the first set of probabilities in combination with at least a subset of the probabilities within the second set of probabilities.
 15. The computer-program product of claim 12, wherein: the language model is based on an n-gram corpus comprising n-grams correlated to corresponding probabilities of use in a pre-selected language; and the use of the language model to generate the second set of probabilities comprises the at least one processor performing operations comprising: for each candidate word of the set of candidate words, generate a corresponding candidate n-gram of a set of candidate n-grams that comprises a combination of the candidate word and at least one preceding word spoken in the speech audio; for each candidate n-gram, search the n-gram corpus for the candidate n-gram to retrieve the corresponding probability for inclusion in the second set of probabilities; determine which candidate n-gram of the set of candidate n-grams corresponds to the highest probability among the second set of probabilities; and indicate the candidate word that corresponds to the candidate n-gram that corresponds to the highest probability of the second set of probabilities as being the second candidate word.
 16. The computer-program product of claim 11, wherein: the acoustic model accepts indications of instances of acoustic features of a pre-selected set of acoustic features occurring within the portion of speech audio; and the use of the acoustic model to generate the first set of probabilities comprises the at least one processor performing operations comprising: use a feature detector to detect instances of occurrence of the pre-selected set of acoustic features to generate at least one feature vector that provides the indications of acoustic features occurring within the portion of speech audio; and provide the at least one feature vector to the acoustic model as input to generate the first set of probabilities.
 17. The computer-program product of claim 16, wherein: the acoustic model is implemented with a neural network; and the neural network comprises a connectionist temporal classification (CTC) output to provide an indication of a pause between speech sounds of the next word for inclusion in the at least one feature vector.
 18. The computer-program product of claim 11, wherein: the first set of probabilities comprises at least one probability distribution indicative of relative probabilities of utterance of each grapheme of a pre-selected set of graphemes at a time during pronunciation of speech sounds of the next word; and analysis of the first set of probabilities to derive the degree of uncertainty comprises deriving a degree of entropy or perplexity for each probability distribution of the at least one probability distribution.
 19. The computer-program product of claim 18, wherein each grapheme of the pre-selected set of graphemes corresponds to a portion of a phoneme, a single phoneme or a combination of phonemes.
 20. The computer-program product of claim 18, wherein: each grapheme of the pre-selected set of graphemes corresponds to a single text character of a pre-selected set of text characters or a combination of text characters of the pre-selected set of text characters; and the pre-selected set of text characters comprises a blank symbol indicative of a pause between speech sounds.
 21. A computer-implemented method comprising: receiving, by at least one processor, and from a requesting device via a network, a request to perform operations comprising speech-to-text conversion of a specified speech data set representing speech audio; and the method comprises, in response to the request, performing speech-to-text processing operations comprising: using, by the at least one processor, an acoustic model to generate a first set of probabilities of speech sounds uttered within a portion of the speech audio; deriving, by the at least one processor, at least a first candidate word most likely spoken in the speech audio using the first set of probabilities; analyzing, by the at least one processor, the first set of probabilities to derive a degree of uncertainty of the first set of probabilities; comparing, by the at least one processor, the degree of uncertainty to a threshold degree of uncertainty; in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, selecting, by the at least one processor, the first candidate word as a next word most likely spoken in the speech audio; in response to at least the degree of uncertainty being greater than the threshold degree of uncertainty, selecting, by the at least one processor, as the next word most likely spoken in the speech audio, a second candidate word that is indicated as being most likely spoken based on a second set of probabilities generated by a language model; and adding the next word most likely spoken to a transcript of the speech audio.
 22. The computer-implemented method of claim 21, wherein: deriving at least the first candidate word comprises deriving a set of candidate words most likely spoken in the speech audio from the first set of probabilities; the set of candidate words comprises: the first candidate word; and the second candidate word; and the second set of probabilities is generated by the language model to correspond to the set of candidate words.
 23. The computer-implemented method of claim 22, comprising, in response to at least the degree of uncertainty being less than the threshold degree of uncertainty, refraining from expending processing resources to use the language model to generate the second set of probabilities.
 24. The computer-implemented method of claim 22, comprising, selecting the first candidate word or the second candidate word based on the degree of uncertainty of the first set of probabilities in combination with at least a subset of the probabilities within the second set of probabilities.
 25. The computer-implemented method of claim 22, wherein: the language model is based on an n-gram corpus comprising n-grams correlated to corresponding probabilities of use in a pre-selected language; and using the language model to generate the second set of probabilities comprises performing operations comprising: for each candidate word of the set of candidate words, generating a corresponding candidate n-gram of a set of candidate n-grams that comprises a combination of the candidate word and at least one preceding word spoken in the speech audio; for each candidate n-gram, searching the n-gram corpus for the candidate n-gram to retrieve the corresponding probability for inclusion in the second set of probabilities; determining which candidate n-gram of the set of candidate n-grams corresponds to the highest probability among the second set of probabilities; and indicating the candidate word that corresponds to the candidate n-gram that corresponds to the highest probability of the second set of probabilities as being the second candidate word.
 26. The computer-implemented method of claim 21, wherein: the acoustic model accepts indications of instances of acoustic features of a pre-selected set of acoustic features occurring within the portion of speech audio; and using the acoustic model to generate the first set of probabilities comprises performing operations comprising: using a feature detector to detect instances of occurrence of the pre-selected set of acoustic features to generate at least one feature vector that provides the indications of acoustic features occurring within the portion of speech audio; and providing the at least one feature vector to the acoustic model as input to generate the first set of probabilities.
 27. The computer-implemented method of claim 26, wherein: the acoustic model is implemented with a neural network; and the neural network comprises a connectionist temporal classification (CTC) output to provide an indication of a pause between speech sounds of the next word for inclusion in the at least one feature vector.
 28. The computer-implemented method of claim 21, wherein: the first set of probabilities comprises at least one probability distribution indicative of relative probabilities of utterance of each grapheme of a pre-selected set of graphemes at a time during pronunciation of speech sounds of the next word; and analyzing the first set of probabilities to derive the degree of uncertainty comprises deriving a degree of entropy or perplexity for each probability distribution of the at least one probability distribution.
 29. The computer-implemented method of claim 28, wherein each grapheme of the pre-selected set of graphemes corresponds to a portion of a phoneme, a single phoneme or a combination of phonemes.
 30. The computer-implemented method of claim 28, wherein: each grapheme of the pre-selected set of graphemes corresponds to a single text character of a pre-selected set of text characters or a combination of text characters of the pre-selected set of text characters; and the pre-selected set of text characters comprises a blank symbol indicative of a pause between speech sounds. 